The study adapted the Cognitive Reflection Test, a classic paradigm in reasoning research. CRT problems are built around a conflict between an intuitively compelling but incorrect response and a less obvious, mathematically correct solution. In the famous bat-and-ball problem, for example, many people instantly answer 0.10 euros, while the correct answer is 0.05 euros. Rather than solving such problems themselves, participants in this study were shown each problem together with a supplied answer and asked simply to judge whether that answer was correct or incorrect. This design shifts the question from problem solving to answer evaluation, a situation that is increasingly common as generative AI systems supply answers in everyday, academic, and professional contexts.

The experiment used a within-subjects design, meaning every participant saw answers under both source labels. Thirty trials presented three categories of answers: CRT problems paired with the reflective, correct solution; CRT problems paired with the intuitive, incorrect solution; and simple arithmetic control problems paired with objectively correct answers. Each source condition contained five trials per category. Two parallel batteries of fifteen matched problems, identical in reasoning structure but differing in numerical values and surface context, were randomly assigned to the ChatGPT or Computer label between participants, and trial order was randomized. The displays were visually matched, differing only in the source label and a small corresponding icon, to minimize perceptual confounds.

An independent pretest with 35 participants, conducted without any source labels, supported the comparability of the two batteries. Mean judgment accuracy for reflective answers was 0.510 in one battery and 0.504 in the other, and for intuitive answers 0.620 and 0.650, with neither difference significant and cross-battery correlations very strong. Control accuracy was perfect in both batteries. The main experiment was administered online, and participants afterward completed questionnaires measuring trust in AI, general attitudes toward artificial intelligence, and dispositional critical thinking. At the end of the session, they were told that all answers had been prepared by the researchers and that the labels had been assigned for experimental purposes.

The headline result came from a participant-level analysis: answers attributed to ChatGPT were accepted more often overall. A trial-level logistic mixed model, which included crossed random intercepts for participants and for the fifteen parallel-problem pairs, revealed a significant source-by-category interaction. The source association was particularly pronounced for the arithmetic control answers, where the odds of acceptance were more than six times higher under the ChatGPT label, with an odds ratio of 6.08 and a 95 percent confidence interval ranging from 2.70 to 13.69. By contrast, the source contrasts within the two CRT categories were not significant, with odds ratios of 1.17 for both reflective-correct and intuitive-incorrect answers.

The control-answer contrast is especially informative because both conditions presented objectively correct arithmetic answers in matched displays, and judging them did not require resolving any intuitive-reflective conflict. Yet acceptance still differed by label: correct control answers were accepted 98.6 percent of the time under ChatGPT versus 92.4 percent under Computer, meaning participants rejected correct arithmetic nearly 1.4 percent of the time in one condition and 7.6 percent in the other. The authors caution that this pattern shows a contribution of attributed source identity to answer evaluation within this task, but it does not identify whether participants held different expectations about the two sources or verified particular answers differently.

Signal-detection analysis added an important nuance. Treating reflective-correct CRT answers as signal trials and intuitive-incorrect answers as noise trials, the researchers separated sensitivity, the ability to discriminate correct from incorrect answers, from response criterion, the willingness to say correct. Mean sensitivity was close to zero under both labels, and neither the sensitivity comparison nor the criterion comparison reached statistical significance, although the criterion was descriptively more liberal under ChatGPT. In other words, the study found greater source-related endorsement, but no evidence that the ChatGPT label improved participants’ actual discrimination between right and wrong CRT answers.

Evaluation times told a similarly qualified story. Raw mean latencies were about one second longer under the ChatGPT label, roughly 12,558 milliseconds versus 11,515 milliseconds, a difference that reached significance in the raw-time analysis. However, because the screened response times were strongly positively skewed, the researchers also ran the analysis on log-transformed times, and there the source difference was not significant. The authors note that longer evaluation could reflect scrutiny, uncertainty, attention to the source label, or hesitation, and that longer elapsed time can still end in the endorsement of an incorrect answer, so latency alone does not demonstrate successful verification.

The exploratory individual-difference analyses detected no significant associations between source-related differences in acceptance, accuracy, or latency and any of the measured dispositions. Trust in AI correlated positively with positive attitudes toward AI and negatively with negative attitudes, but neither trust nor attitudes nor critical-thinking disposition tracked how much more strongly a given participant endorsed ChatGPT-labeled answers. The authors stress that these correlations are descriptive and that the behavioral difference scores showed very low internal consistency, so the null findings cannot establish that these characteristics are unrelated to source effects.

The study has clear limitations. Participants were recruited by convenience, five trials per source-by-category cell is a brief assessment, and answer category and objective truth were not fully crossed, since reflective and control answers were correct while intuitive answers were incorrect. The comparison involved two technological sources, a named conversational AI and a generic computer, so the results do not show how either would fare against a human expert or an unlabeled answer, and prior work has documented both algorithm appreciation and skepticism toward AI-labeled content depending on task and domain. Even so, the findings sharpen an increasingly urgent question: as generative AI becomes embedded in contexts where verification carries real consequences, a simple source label can measurably raise the odds that people accept an answer, without any measurable improvement in their ability to tell correct answers from wrong ones.

Subject of Research: The effect of ChatGPT source attribution on answer acceptance in a cognitive reflection and arithmetic evaluation task

Article Title: The ChatGPT source cue shapes answer acceptance in a reasoning task

Article References: Fabio, R. A., Rantuccio, E., & Bruno, F. (2026). The ChatGPT source cue shapes answer acceptance in a reasoning task. Frontiers in Psychology, 17, Article 1923236. https://doi.org/10.3389/fpsyg.2026.1923236

Image Credits: AI Generated

DOI: 10.3389/fpsyg.2026.1923236

Keywords: ChatGPT, source attribution, Cognitive Reflection Test, evaluative judgment, answer acceptance, dual-process reasoning, trust in AI, signal detection, algorithm appreciation, critical thinking, generative AI, cognitive psychology

The study recruited English-speaking participants through Prolific and the undergraduate participant pool of a large North American university. The initial sample comprised 204 people: 100 Prolific participants and 104 students. After responses considered omitted or nonsensical were removed, the analytic sample included 169 answers to the AI-assistant question and 175 answers to a question about trust in general. The AI responses ranged from 1 to 101 words, with a mean of 15.32 words, while general-trust responses ranged from 1 to 85 words and averaged 12.65 words. Researchers used inductive qualitative content analysis within a postpositivist framework, breaking responses into meaningful semantic units and allowing categories to develop from participants’ wording rather than imposing a predefined model of trust.

Performance was by far the dominant dimension. Participants described an AI assistant as trustworthy when it was accurate, dependable, competent, consistent and able to complete requested tasks effectively. Among the performance-related references, 57% concerned accuracy, 17% concerned reliability, 7.6% referred to source quality and 5.5% concerned competence. Smaller groups mentioned helpfulness, behaving as intended, transparent reasoning, consistency and the ability to depend on the system. Participants did not want an assistant merely to produce a plausible answer: they expected information to be correct, factually reliable and connected to reputable, up-to-date sources. Some also wanted the assistant not to fabricate information simply to respond. This prominence distinguishes the spontaneous definitions obtained in the study from earlier multidimensional measures that grouped capability and reliability together without explicitly treating accuracy as a separate concern.

Safety was the second major theme, appearing in about 38% of AI-trust responses and most often in connection with privacy and confidentiality. Participants said they expected shared information to remain confidential, personal disclosures to be protected and the assistant to prioritize the user’s interests rather than pursue concealed agendas. Some described trust as feeling secure about what they type into the system, while others said they would accept an answer without continually checking it. The latter view reflects a stated willingness to suspend doubt and resembles the human-factors concept of automation bias, in which people accept automated outputs uncritically. The safety category therefore captured both a request for protective safeguards and, in some cases, a readiness to rely on the assistant without further verification.

Perceived moral integrity accounted for 26% of AI-trust responses. Participants associated trustworthy AI with being honest, unbiased, fair and impartial, as well as free from hidden motives. They also sometimes directed these expectations toward the organization or regulatory framework behind an assistant rather than describing the model itself as a moral agent. At the same time, several participants explicitly noted that AI does not possess emotions or motives like a human and therefore considered reliability, consistency and the integrity of its output more relevant. The analysis distinguished honesty from transparency: honesty meant that an assistant told the truth and was not perceived as deceptive, while transparency meant that it disclosed information about its operation, sources or data use. A system could therefore be regarded as transparent without being trusted to tell the truth, or as honest without being transparent.

Comparing AI trust with participants’ broader conception of trust showed a shift in emphasis rather than an entirely different set of ideas. Performance remained the leading category in general trust, but it appeared in 51% of responses, well below the 85% recorded for AI. Integrity and character were mentioned in 36% of general-trust responses, safety in 31%, and a separate category that the researchers labeled faith in 12.5%. Within the general concept of trust, respondents more often emphasized reliability, dependability, honesty and loyalty. Accuracy was comparatively uncommon, whereas faith described accepting an answer without proof or investigation. Loyalty and fears of being betrayed were prominent in descriptions of general trust, but the reported AI responses did not establish betrayal as a defining expectation for AI assistants. The comparison suggests that people retain broad interpersonal ideas about trust but place greater weight on functional quality when the object of trust is an AI assistant.

The results are compatible with two complementary perspectives used in human–AI research. A systems perspective treats trust as confidence in mechanisms, standards, accountability structures and technological infrastructure, much as a rideshare passenger may rely on ratings, screening and complaint procedures rather than personal knowledge of a driver. An interpersonal perspective emphasizes competence, benevolence and integrity and helps explain why some participants assigned quasi-moral qualities to AI. Performance comments often appeared to concern the broader system’s capacity to generate dependable outputs, while expectations about privacy, source disclosure and data use could point toward the company or infrastructure supporting the assistant. At the same time, demands that an AI never fabricate information, prioritize the user’s goals or act without hidden agendas can sound like moral accountability directed at an autonomous actor. The open-ended responses do not establish that AI trust is fundamentally interpersonal or technical; they show that both forms of reasoning were present in the same population.

Participants also linked trust to transparency, helpfulness and appropriate behavior. Requests for understandable reasoning and clear disclosure of sources were consistent with arguments that transparency can support trust in AI, although the survey cannot determine whether transparency caused confidence or merely appeared alongside it. Helpfulness mattered in practical terms: some participants said they would extend trust after an assistant had demonstrated useful and genuinely supportive assistance. Competence was expressed as completing a task efficiently, while “behaving as intended” meant doing what was requested and nothing else. The design of the study is important because it began with participants’ spontaneous descriptions rather than asking them to rate predefined trust dimensions. That approach exposed accuracy as a particularly prominent concern and revealed the coexistence of technical, protective and moral expectations. It did not test whether any one attribute changes behavior, reliance or willingness to use an assistant.

Several limitations constrain the conclusions. The cross-sectional design cannot establish that accuracy, safety, integrity or any other attribute causes trust to form, and it cannot show how attitudes change over time. Brief open-ended answers offered broad coverage but less interpretive depth than interviews. Recruitment through online platforms and a university participant pool used convenience sampling: the Prolific group included 56 people from the United States, 26 from the United Kingdom, 17 from Canada and one from France, while the remaining participants were undergraduates. Online recruitment may also have favored people who were digitally engaged and willing to take part in AI research, potentially underrepresenting other attitudes. In addition, participants saw a standardized definition of an AI assistant and answered other questions about generative AI before the open-ended items, so contextual priming cannot be excluded. Methodological checks nevertheless showed substantial coder agreement: Cohen’s kappa ranged from 0.68 to 0.78 across major codes, with percent agreement from 86% to 95%.

The study’s main contribution is an empirically grounded map of the expectations that these English-speaking participants brought to a relatively new trust question. Their descriptions place accurate, dependable and useful performance at the center, but do not stop there. They also ask for privacy, protection from harm, freedom from hidden agendas, resistance to bias, honesty and accountable disclosure of sources or data use. Because many responses crossed more than one category, lay trust in AI cannot be reduced to whether an assistant sounds truthful or performs a task successfully. The findings provide a starting framework for future measurement and design work, while longitudinal studies, experimental manipulations and in-depth interviews would be needed to test how the identified dimensions relate to trust formation, reliance and behavior over time and across different populations and applications.

Subject of Research: Laypeople’s conceptual dimensions of trust in AI assistants

Article Title: Identifying conceptual dimensions of trust in artificial intelligence from qualitative content analysis of open-ended responses

Article References: Toudjui, S., Tang, J., Akhter, H., Cho, E., Côté, É., & Plaks, J. E. (2026). Identifying conceptual dimensions of trust in artificial intelligence from qualitative content analysis of open-ended responses. Discover Artificial Intelligence, 6(1), Article 1036. https://doi.org/10.1007/s44163-026-02150-x

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02150-x

Keywords: artificial intelligence, trust in AI, generative AI, qualitative analysis, open-ended survey, AI performance, AI safety, privacy, moral integrity, accuracy, Identifying, conceptual

The central challenge was not simply to predict an answer, but to preserve the psychological meaning of the model while improving its forecasts. Trust in output accuracy and favorability toward AI represent related but distinct appraisals: a developer might believe a tool’s output is accurate without welcoming its use, or might like using AI while remaining uncertain about its reliability. In a conventional one-stage regression, adding occupation, age, country, AI exposure, or perceived job threat can change the estimated coefficients for trust and favorability. Retuning the entire model for a different error measure can also alter those coefficients. TARS separates the two functions. Its Stage 1 is a polynomial ridge regression containing trust, favorability, and their interaction and quadratic terms; Stage 2 is another ridge regression that predicts Stage 1 residuals from 66 encoded context variables. Because the context matrix and Stage 2 tuning choices do not enter the Stage 1 equation, the theoretical relationship remains fixed even when the contextual predictors or prediction loss change.

The study used a single survey question as its outcome: “How well do the AI tools you use in your development workflow handle complex tasks?” Responses ranged from 1, meaning “very poor,” to 5, meaning “very well.” The authors analyzed the ordered item directly and supplemented continuous error measures with quadratic weighted kappa, which assesses agreement between predicted and observed categories after predictions are rounded to the scale. Trust in AI output accuracy and favorability toward using AI tools were also measured with matching five-category items in both years. The analysis identified itself as observational: it estimated associations and predictive performance, not causal effects or changes within individual developers. The samples consisted of different people in each survey wave, and the reported cross-year differences in outcome distributions cannot establish a population trend over time.

Sample construction produced an important boundary on the findings. Starting from 65,437 retained records in the official 2024 file and 49,191 in 2025, the researchers selected respondents who identified as developers by profession, met the respective year-specific employment criterion, and answered all three required items with evaluable responses. The 2024 rule required full-time employment, whereas the redesigned 2025 item required employment more generally. Missing responses, an “I don’t know” outcome category in 2025, and an “Unsure” favorability response were excluded. The final samples represented 34.78% of the official 2024 records and 34.60% of the 2025 records. No respondents were removed as multivariate outliers. Complete-item selection may create selection or collider bias if AI attitudes, tool use, occupation, or perceived capability influence which questions people answer, so the results apply most directly to eligible complete responders rather than to all developers or workers.

Across both years, higher trust in AI accuracy and greater favorability toward AI were associated with higher perceived capability. Trust had the stronger association. The response-surface analysis quantified this pattern: the line-of-congruence slope, describing the change when trust and favorability rise together, was 0.702 in 2024, with a 95% confidence interval of 0.684 to 0.720, and 0.792 in 2025, with an interval of 0.775 to 0.809. The line-of-incongruence slopes were also positive, 0.249 in 2024 and 0.210 in 2025, indicating that trust carried greater weight when the two ratings differed. The corresponding curvature intervals included zero in both years, so the analysis did not provide evidence of a general mismatch penalty. The directional comparison between trust and favorability was not preregistered and was therefore treated as exploratory, while the jointly increasing relationship was the principal theoretical component carried into TARS.

The strict 2024-to-2025 evaluation showed small but consistent advantages for the two-stage design. TARS produced the highest R² point estimate and the lowest RMSE and MAE point estimates among the source-only models tested. Its RMSE was 0.911, with a 95% confidence interval of 0.902 to 0.921, while quadratic weighted kappa was 0.584, with an interval of 0.574 to 0.592. Compared with one-stage response-surface ridge regression, the paired R² gain was 0.00179, with an interval of 0.00131 to 0.00221; MAE fell by 0.00643, and kappa rose from 0.573 to 0.584. The model also outperformed a trust-only ridge, a context-only ridge, elastic net, random forest, histogram gradient boosting, and a multilayer perceptron on the main point-estimate comparison. These were modest differences rather than a dramatic separation from the strongest baselines.

Model order provided further evidence that the two stages did different jobs. A one-stage model with separate penalties for the trust–favorability and context terms reached an R² of 0.444 and an MAE of 0.748, compared with 0.445 and 0.742 for TARS. A context-first two-stage model, which fitted context before modeling the remaining trust and favorability relationships, was weaker, with an R² of 0.440 and MAE of 0.749. Removing the context correction entirely reduced R² to 0.435 and increased MAE to 0.757, indicating that contextual information improved prediction beyond the theoretical terms alone. Changing the set of context variables produced exactly identical TARS Stage 1 coefficients across four configurations: work and AI-use context, demographic context, all non-geographic context, and the complete set. A comparable one-stage model changed a Stage 1 coefficient by as much as 0.0202 when context changed, while all 16 paired performance intervals across the tested configurations favored TARS.

The researchers also tested whether the second stage could target absolute category error without disturbing the first. TARS-RMSE and TARS-MAE reused the same polynomial Stage 1 model, trust–favorability coefficients, and Stage 1 penalty; only the Stage 2 ridge penalty and its contribution to the final score were retuned. The maximum absolute difference among the Stage 1 coefficients was zero. On the 2025 target, the MAE-tuned version reduced MAE by 0.00134 relative to the RMSE-tuned version, with a 95% confidence interval of 0.00102 to 0.00168, while differences in R² and RMSE were small and their intervals included zero. This demonstrates the separation of concerns directly: an organization can emphasize squared error or mean absolute error in the contextual correction while retaining the same estimated trust and favorability relationship for interpretation.

Generalization checks and computational results strengthened the practical picture, although they did not eliminate the study’s observational limits. TARS had lower MAE point estimates than one-stage response-surface ridge regression in all 13 audited target groups, comprising five role families and eight country groupings, and every paired MAE interval favored TARS. In a tougher leave-one-group-out analysis, the evaluated role or country group was removed before source tuning and fitting, so it contributed no labeled members to model development. Across 13 such tasks, TARS-MAE still produced lower MAE than an independently MAE-tuned one-stage model, with all paired intervals below zero; the median reduction was 0.00793. Its RMSE was lower in 11 tasks, with 10 intervals excluding zero. The full preprocessing, fitting, and prediction pipeline for 19,961 target rows had a median runtime of 0.312 seconds in the audited environment, compared with 0.294 seconds for one-stage ridge regression and substantially longer times for the evaluated nonlinear ensembles.

The result offers a compact method for repeated monitoring surveys, but it does not establish that AI tools objectively perform better or worse, nor that trust causes favorable attitudes or perceived capability. The measures are single self-reports collected at the same time, their temporal order is unknown, and the developers were self-selected through Stack Overflow’s recruitment channels. The two years also differed in some response formats, including the employment item and a non-evaluable 2025 outcome category, so the lower mean capability rating in 2025—2.666 compared with 2.798 in 2024—may reflect respondent composition, selection, or instrument changes as well as different appraisals. The strongest supported conclusion is narrower and still notable: when an interpretable trust–favorability relationship must remain stable, a source-only two-stage model can use contextual information to improve prediction of a future survey wave while preserving that relationship. For organizations monitoring developer attitudes toward AI, TARS provides a transparent, fast, and auditable option, while its aggregate appraisal scores should not be repurposed for employee selection or performance management.

Subject of Research: Source-only cross-year prediction of developers’ perceived AI capability using trust, favorability, and contextual variables.

Article Title: Source-only cross-year prediction of perceived AI capability with a theory-anchored, loss-specific two-stage model

Article References: Wang, D., & Song, Y. (2026). Source-only cross-year prediction of perceived AI capability with a theory-anchored, loss-specific two-stage model. Frontiers in Psychology, 17, Article 1867910. https://doi.org/10.3389/fpsyg.2026.1867910

Image Credits: AI Generated

DOI: 10.3389/fpsyg.2026.1867910

Keywords: artificial intelligence, developer surveys, prediction, trust in AI, favorability, cross-year transfer, ridge regression, response surface, Stack Overflow, Source-only, cross-year, perceived

The study, published in BMC Health Services Research , examined both reported user experiences and community responses. Researchers used BERTopic, a topic-modeling method, to identify recurring themes in the retained posts, producing 14 themes. They then coded posts along four dimensions: the purpose of AI use, emotional change attributed to chatbot responses, trust stance and verification orientation, and the reported relationship to professional care.

A separate classification of 43,899 comments assigned them to nine response functions. These automated classification tasks were validated against human-coded samples, with reported accuracy ranging from 0.79 to 0.94 and macro-F1 scores from 0.68 to 0.91. Emotion was divided in the accounts posted by users.

In 26.3% of retained posts, authors said chatbot responses changed how they felt: 12.6% described relief and 11.5% described alarm. These percentages represent emotional changes attributed by post authors, not measured effects caused by the chatbots. The analysis also found substantial caution and information checking.

In 42.4% of posts, authors expressed distrust or caution about chatbot advice or said they continued checking it against other sources, whereas 8.9% described treating a chatbot as their main source. The findings therefore portray users comparing AI responses with other health information rather than consistently accepting them as definitive. Comments revealed differences among the selected communities.

Among comments assigned a substantive response function, sharing health information or personal experience was most common in the medical-advice subreddits and in r/ClaudeAI. In three other AI-product subreddits, the most frequent responses presented chatbots as limited aids or warned about their limitations. In r/HealthAnxiety, the most common response function discouraged repeated reassurance seeking.

The study therefore documents how communities interpreted and contextualized AI-generated health advice, but it does not show that any particular community response improved health outcomes. Overall, the research offers a bounded snapshot of self-reported behavior in eight selected Reddit communities rather than a measure of the general public or a clinical evaluation of chatbot performance. Because the corpus consisted of public Reddit posts and comments, the results cannot establish whether chatbot advice caused the reported feelings, whether it was medically accurate or whether it affected diagnoses or treatment decisions.

The analysis also found that professional-care pathways dominated the selected accounts: 24.0% of posts described seeking, continuing or obtaining more urgent care, compared with 4.6% that described replacing, delaying, avoiding or reducing care. Within this corpus, generative AI was most frequently presented not as an autonomous doctor substitute, but as an interpretive tool whose output users questioned, compared with other information and discussed with peers.

Subject of Research: Content analysis of Reddit experiences with and community responses to generative AI health advice

Article Title: User experiences and community responses to generative artificial intelligence health advice on reddit: topic modeling and content analysis study

Article References: Tang, Z., He, G., & Pan, X. (2026). User experiences and community responses to generative artificial intelligence health advice on reddit: topic modeling and content analysis study. BMC Health Services Research. https://doi.org/10.1186/s12913-026-15613-9

Image Credits: AI Generated

DOI: 10.1186/s12913-026-15613-9

Keywords: generative artificial intelligence, health chatbots, Reddit, online health information, health anxiety, trust in AI, professional care, topic modeling, BERTopic, care-seeking, User, experiences

The research team built their investigation around a structural equation model, a statistical framework that allows researchers to test networks of hypothesized cause-and-effect relationships simultaneously rather than one variable at a time. Using SmartPLS software, the standard tool for partial least squares structural equation modeling, they evaluated how generative AI usage affects creativity both directly and indirectly through three mediating variables: AI self-efficacy, AI literacy, and AI affordance. AI self-efficacy refers to a student’s belief in their own capacity to use AI tools effectively, a concept rooted in Bandura’s social cognitive theory. AI literacy captures knowledge and competence concerning AI systems, including awareness of their limitations. AI affordance describes the perceived action possibilities that AI tools offer, the sense of what a user can actually accomplish with them.

The findings confirmed that generative AI usage has a statistically significant effect on engineering students’ creativity, with a test statistic exceeding the conventional significance threshold of 1.96 and a p-value below 0.05. The effect size fell in what researchers classify as the moderate range, between 0.15 and 0.35, meaning the relationship is real and meaningful but not overwhelming. More revealing than the direct effect, however, was the mediation structure. The impact of generative AI usage on creativity was transmitted primarily through AI self-efficacy and AI affordance, both of which showed moderate downstream effects on creative outcomes. AI literacy, by contrast, carried a notably weaker indirect effect, even though generative AI usage strongly influenced all three mediators.

This asymmetry between the mediators is one of the study’s most provocative results. Students who used generative AI tools developed stronger confidence, sharper literacy, and richer perceptions of affordance across the board, yet only confidence and perceived affordance translated efficiently into creative performance. Literacy, the researchers suggest, may function more as background knowledge than as an active engine of creativity. Knowing how a large language model works, in other words, does not necessarily mean a student will push it toward novel engineering solutions. What matters more is whether students feel capable of wielding the tool and whether they perceive it as opening genuine possibilities for design and problem-solving.

The study’s second analytical layer addressed trust, and here the design became more sophisticated. Rather than treating trust as a single average effect across all participants, the team split the sample into two groups: a trust-in-AI group and a distrust-in-AI group. Multigroup analysis, a technique that tests whether structural paths differ significantly between subpopulations, revealed that the effects of trust in AI on the relationships between generative AI usage and the three mediators were significantly stronger in the trusting group than in the distrusting group. In practical terms, students who trusted AI extracted more psychological benefit from every interaction with the technology, while skeptical students gained less from the same usage.

This trust-dependent architecture has implications that ripple well beyond engineering education. The result echoes a growing body of literature on the double-edged nature of generative AI in creative domains. Earlier work published in Current Psychology documented paradoxical tensions in how employees use generative AI, finding that the same tools can either support or undermine creative output depending on how they are deployed. Research on ChatGPT dependence has linked inert thinking to overreliance on chatbots, while studies of creative self-efficacy in STEM education argue that beliefs about one’s own creative capacity are central to whether students engage in creative work at all. The new study threads these findings together by showing that trust modulates the entire psychological pipeline from tool usage to creative belief to creative behavior.

The engineering context matters. Engineering creativity is typically characterized as functional creativity, the generation of novel solutions to complex technical problems, a concept developed in the systems models of Cropley and colleagues. Unlike artistic creativity, engineering creativity must satisfy constraints of feasibility, utility, and safety, which makes it a demanding test case for whether generative AI can enhance human ingenuity rather than merely accelerate routine output. Prior systematic reviews have identified fostering creativity in science and engineering education as a persistent challenge, and the arrival of large language models has forced educators to reconsider how creative skills are taught and assessed. The Guangzhou study contributes a psychological mechanism to this debate: generative AI does not act on creativity directly so much as it acts on students’ self-beliefs and perceived possibilities, which then act on creativity.

Methodologically, the research follows established conventions for survey-based structural modeling in behavioral research, including attention to common method biases, a concern documented extensively by Podsakoff and colleagues in their influential review of bias remedies in behavioral research. The study received ethical approval from the Ethics Review Committee of the School of Education at Guangzhou University, and informed consent was obtained from all participants. The authors report no conflicts of interest, and the work was supported by a 2023 Special Task Project of Humanities and Social Sciences Research by the Ministry of Education of China. The raw data supporting the study’s conclusions will be made available by the authors without undue reservation.

For educators and policymakers, the practical message is that simply providing access to generative AI tools is unlikely to be sufficient. If creativity gains flow through self-efficacy and perceived affordance, then training programs should target those beliefs directly, building students’ confidence through scaffolded practice and demonstrating concrete creative possibilities that the tools enable. The weak indirect effect of AI literacy suggests that literacy curricula, while valuable for risk awareness and ethical understanding, should not be expected to drive creative outcomes on their own. And the trust findings imply that institutional culture around AI, including transparency about how systems work and honest discussion of their failure modes, may shape how much psychological benefit students derive from usage.

The study also leaves open questions that future research will need to address. The moderate effect sizes indicate that generative AI usage explains only part of the variance in engineering creativity, leaving room for factors such as team dynamics, prior creative experience, and disciplinary context. The trust-based grouping raises the question of what causes students to trust or distrust AI in the first place, a topic explored in recent chain-mediation work linking AI literacy to perceived trust. As generative AI becomes embedded in engineering curricula worldwide, the Guangzhou findings suggest that the decisive variable may not be the sophistication of the model but the psychology of the student holding the prompt, a conclusion that reframes the debate about AI and creativity from a question about machines to a question about minds.

Subject of Research: The psychological mechanisms linking generative AI usage to creativity in engineering students

Article Title: Engineering creativity through generative artificial intelligence: the role of artificial intelligence self-efficacy, literacy, affordance, and trust

Article References: Guo, H., Zhou, Z., & Ma, F. (2026). Engineering creativity through generative artificial intelligence: the role of artificial intelligence self-efficacy, literacy, affordance, and trust. Current Psychology, 45(20), Article 1634. https://doi.org/10.1007/s12144-026-10177-7

Image Credits: AI Generated

DOI: 10.1007/s12144-026-10177-7

Keywords: generative artificial intelligence, engineering creativity, AI self-efficacy, AI literacy, AI affordance, trust in AI, structural equation modeling, engineering education, Current Psychology, Guangzhou University, social cognitive theory, creativity research

The study drew on three theoretical strands: andragogical learning theory, which frames adult learners as self-directed and goal-oriented; technology acceptance research, which positions perceived usefulness as a central driver of adoption; and trust-in-automation scholarship, which treats trust as an affective-cognitive judgement about a system’s dependability. Within this framework, the researchers operationalised epistemic awareness narrowly as students’ awareness that generative AI can produce inaccurate information, lacks human-like understanding, and generates responses through pattern-based prediction rather than factual reasoning. This is distinct from broader AI literacy, which encompasses technical knowledge, prompt engineering, ethics, and operational competence.

The researchers tested six hypotheses. The first five proposed linear structural relationships: that epistemic awareness positively predicts trust, that perceived usefulness positively predicts trust, that trust positively predicts frequency of AI use, that AI use positively predicts learning engagement, and that AI use mediates the relationship between perceived usefulness and learning engagement via trust. The sixth hypothesis proposed a curvilinear over-trust effect, suggesting that extremely high trust would be associated with reduced learning engagement. Because this final hypothesis concerned a within-construct nonlinear effect rather than a linear path between latent constructs, it was evaluated through a supplementary quadratic regression conducted outside the core structural model.

The results were striking. Perceived usefulness emerged as the primary antecedent of trust, with a standardised path coefficient of 0.856 (p < 0.001). Trust, in turn, strongly predicted AI use (β = 0.860, p < 0.001), and AI use strongly predicted learning engagement (β = 0.700, p < 0.001). The bootstrapped indirect pathway from perceived usefulness through trust and AI use to learning engagement was significant (β = 0.515, p < 0.001), supporting the mediation hypothesis. The structural model explained 78.1 percent of the variance in trust, 73.9 percent in AI use, and 49.0 percent in learning engagement.

But the hypothesis that epistemic awareness would positively predict trust was not supported. The coefficient was negative in direction (β = −0.098) but statistically non-significant (p = 0.172), meaning the data provided no evidence that greater awareness of AI limitations increases or decreases trust. The researchers are careful to note that this non-significant finding should not be interpreted as proof that epistemic awareness necessarily produces cautious trust; rather, it indicates that the hypothesised positive association was simply not detectable in this sample. Alternative factors such as perceived risk, AI scepticism, prior experience, or other unmeasured characteristics may contribute to how awareness relates to trust, and future research using behavioural, longitudinal, or process-based measures could examine this more directly.

The supplementary nonlinear analysis also failed to support the over-trust hypothesis. Adding a quadratic trust term did not improve model fit relative to the linear model (ΔF = 0.34, p = 0.561), and the quadratic coefficient was not statistically significant (β = 0.020, p = 0.561). Visual inspection of the trust-engagement plot suggested a predominantly positive association rather than the inverted-U pattern the researchers had predicted. Interestingly, once AI use and perceived usefulness were entered as covariates in the supplementary model, the linear effect of trust on engagement became negative and marginally significant (β = −0.193, p = 0.050). The researchers flag this as a pattern worth future investigation, but caution that it does not support the over-trust hypothesis as originally framed, which predicted curvature rather than a negative linear effect.

The measurement model revealed important caveats. Epistemic awareness showed marginal convergent validity, with an average variance extracted of 0.453, falling just below the conventional 0.50 threshold. More significantly, the cluster of trust, perceived usefulness, and AI use failed the Fornell-Larcker discriminant validity criterion, and heterotrait-monotrait ratios between these constructs approached the conventional threshold. This means these three constructs are not fully empirically distinguishable in this sample, and the strength of the structural paths linking them may be partly inflated by shared variance among closely worded items. The researchers interpret the usefulness-trust-use pathway as evidence of a coherent instrumental-adoption process rather than proof of three fully independent psychological mechanisms.

Common method bias was assessed using Harman’s single-factor test, in which all retained indicators were loaded onto a single latent factor. This single-factor model showed poor fit (CFI = 0.718, RMSEA = 0.121), indicating that a single underlying factor could not account for the covariance among items. Multicollinearity among the predictors of trust was assessed using variance inflation factors, both returning values of 1.04, well below the conventional threshold of 5. The confirmatory factor analysis showed acceptable fit (CFI = 0.898, RMSEA = 0.075, SRMR = 0.057), while the structural model fit was more cautious (CFI = 0.880, RMSEA = 0.080), which the researchers interpret as acceptable rather than strong support for the hypothesised structure.

The study’s theoretical contribution lies in positioning generative AI use as a process of cognitive regulation rather than simply a technology-adoption behaviour. The findings suggest that engagement emerges not simply from access to AI, but from a process in which students evaluate usefulness, calibrate trust, and integrate AI into their learning behaviour. Epistemic awareness, rather than operating as a direct positive predictor of trust or adoption, may instead function as an epistemic orientation or critical awareness framework that shapes how students evaluate AI-generated information. This challenges simplistic assumptions that greater awareness of AI limitations necessarily increases acceptance or reliance, and supports emerging arguments that AI literacy should be understood as involving critical evaluation, ethical awareness, and metacognitive judgment rather than operational skill alone.

Practically, the findings suggest that educators and institutions may benefit from designing AI-supported learning activities around clearly defined academic purposes and demonstrable learning value, rather than treating AI use as an end in itself. Because the study was cross-sectional, these findings should be interpreted as associations rather than evidence that increasing perceived usefulness will necessarily cause greater trust or engagement. The researchers recommend that epistemic awareness initiatives prioritise critical awareness rather than merely operational skill, pairing instruction in prompt engineering with explicit training in critically evaluating and verifying AI-generated outputs. They also caution that the absence of a significant nonlinear effect should not be interpreted as evidence that excessive reliance on generative AI is harmless; rather, it indicates that the current sample did not exhibit a measurable decline in engagement at higher trust levels, and longitudinal, experimental, or task-based research may be better positioned to detect such effects under different educational conditions.

Subject of Research: Psychological and behavioural factors linking epistemic awareness, perceived usefulness, trust, and learning engagement in generative AI use among Indian higher education students

Article Title: Understanding learning engagement in Indian higher education through epistemic awareness, perceived usefulness, and trust in generative AI

Article References: Singh, M. I., Pradhan, S., Naveen, S., & Vinod, L. (2026). Understanding learning engagement in Indian higher education through epistemic awareness, perceived usefulness, and trust in generative AI. Discover Education, 5(1), Article 806. https://doi.org/10.1007/s44217-026-02090-9

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02090-9

Keywords: generative AI, higher education, learning engagement, epistemic awareness, trust in AI, perceived usefulness, structural equation modelling, Indian students, technology acceptance, AI literacy, over-trust hypothesis, Discover Education

The research team, led by Lotte Schuitmaker and colleagues in the Department of Bioethics and Health Humanities, conducted in-depth, semi-structured interviews with 17 neonatologists at a Dutch academic hospital. Using thematic analysis, a rigorous qualitative method that systematically codes interview transcripts to identify recurring patterns of meaning, the researchers distilled the physicians’ perspectives into three overarching themes: trusting AI in practice, maintaining control over trusted AI systems, and the limits of trust. The study protocol was reviewed by the research quality control committee of the University Medical Center Utrecht and deemed exempt from the Medical Research Involving Humans Act under research proposal number 23U-0364, and the work was carried out in accordance with the Declaration of Helsinki.

Perhaps the most technically significant finding is the physicians’ insistence on a conceptual distinction that has often been blurred in the AI ethics literature: the difference between trust, confidence, and reliance. In the philosophical sense the neonatologists invoked, trust implies a relationship in which the trusted party is expected to act in the trustor’s interest even under uncertainty, whereas confidence describes a settled expectation based on evidence of performance, and reliance describes the practical act of depending on a system’s output. The neonatologists argued that these are not interchangeable. They can rely on an AI tool’s output without trusting it, and they can trust a colleague’s judgment without relying on it in every case. This distinction matters because much of the current debate about medical AI treats the problem as one of calibration—getting physicians to have the right degree of confidence in an algorithm’s accuracy. The Utrecht interviews suggest the real challenge is deeper: physicians want to know what kind of relationship they are entering into with these systems before they decide whether trust is appropriate at all.

Across the interviews, one condition for trust emerged as non-negotiable: AI’s role must be supplemental to the physician’s own role. The neonatologists welcomed AI in neonatal clinical settings overall, but only on the understanding that the technology augments rather than replaces human judgment. This is not mere professional self-protection. In neonatology, where patients cannot communicate their symptoms and clinical data streams continuously from monitors, ventilators, and laboratory tests, the physician’s integrative judgment—weighing ambiguous signals against the individual patient’s trajectory—is precisely the kind of reasoning that current AI systems, however impressive in narrow imaging tasks, cannot fully replicate. The specialists’ insistence on supplementarity reflects a technically informed understanding of where AI’s strengths and weaknesses lie in data-rich but context-heavy medical domains.

The second pillar of trust identified in the study is validation combined with adequate oversight. The physicians considered rigorous validation of AI systems essential before those systems earn a place in bedside decision-making, and they regarded ongoing human oversight as equally indispensable. This aligns with the well-documented gap between AI’s technical advancement and its practical use in clinical care: a disparity persists between what algorithms can demonstrably do in controlled evaluations and what they can safely do in the messy, evolving conditions of real patient care. The neonatologists’ demand for validation and oversight is a demand for evidence that performance holds up outside the laboratory, and for institutional structures that keep humans in the loop as systems evolve.

Equally important to the physicians was preserving human expertise while working with AI in clinical settings. This concern goes beyond the immediate question of whether an algorithm’s output is accurate. If AI systems gradually take over parts of clinical reasoning, the physicians worried, the expertise needed to supervise those systems—and to catch their failures—could erode. This is a technically recognized phenomenon in human factors research: skills that are rarely exercised degrade, and physicians who habitually defer to algorithmic recommendations may lose the very competence that makes their supervision meaningful. The Utrecht interviews show that neonatologists see this erosion risk as a genuine limit on how much responsibility AI can be given, not as a vague anxiety about change.

The third theme, the limits of trust, surfaced most frequently in the form of two specific worries: overreliance and the incompatibility of clinical intuition with AI in practice. Overreliance—the tendency of humans to accept algorithmic outputs even when those outputs are wrong—is a documented failure mode in human-AI interaction, and the neonatologists named it as a primary reason they might lose trust in AI systems. The second worry is subtler and arguably more consequential for the future of medical AI. Clinical intuition in neonatology is built on years of pattern recognition across thousands of patient encounters, integrating signals that resist formalization into training data. When an AI recommendation conflicts with that intuition, the physician faces a genuine epistemic dilemma: the algorithm may be drawing on population-level patterns the physician has not consciously registered, or it may be failing to capture context the physician knows matters. The interviews reveal that neonatologists experience this incompatibility not as a bug to be engineered away but as a structural feature of how AI and human reasoning differ.

The study’s conclusions carry practical weight for hospitals and regulators moving to deploy AI in neonatal care. The researchers recommend clarifying what is expected of physicians in terms of reliance on AI at the bedside, and formulating concrete guidance for trust in AI. This is a pointed recommendation: in the absence of explicit institutional guidance, individual physicians are left to improvise their own standards for when to accept algorithmic output, when to override it, and when to distrust it entirely. The Utrecht findings suggest that improvisation is already happening, guided by the physicians’ own conceptual frameworks of trust, confidence, and reliance—but that hospitals would do better to make these expectations explicit rather than leave them to bedside discretion.

What makes this study resonate beyond neonatology is its methodological honesty. Rather than surveying physicians with predetermined questions about AI acceptance, the researchers let the themes emerge from open-ended conversation, capturing the ambivalence that quantitative surveys often flatten. The result is a portrait of physicians who are neither AI enthusiasts nor AI skeptics: they welcome the technology’s promise to improve patient outcomes, increase efficiency, and reduce healthcare costs, while insisting on conditions—supplementarity, validation, oversight, preserved expertise—that current deployment strategies often treat as afterthoughts. As AI expands from radiology into neonatology and other data-rich domains, the Utrecht interviews offer a technically grounded answer to the question that will determine medical AI’s real-world success: not whether the algorithms are accurate enough, but whether physicians can determine when they can reasonably be trusted.

Subject of Research: Physicians' trust in medical AI in neonatology

Article Title: Trust in artificial intelligence at the bedside: a qualitative interview study with neonatologists

Article References: Schuitmaker, L., Drogt, J., Hartman, S. D., & Jongsma, K. (2026). Trust in artificial intelligence at the bedside: a qualitative interview study with neonatologists. BMC Pediatrics. https://doi.org/10.1186/s12887-026-07890-4

Image Credits: AI Generated

DOI: 10.1186/s12887-026-07890-4

Keywords: artificial intelligence, neonatology, medical AI, clinical decision-making, trust in AI, qualitative research, BMC Pediatrics, healthcare ethics, Trust, artificial, intelligence, bedside

The study, published in Discover Education, focused on sixty-two second-year computer science education students drawn from two government-owned universities in a developing African context. Programming is notoriously difficult to teach; introductory courses are frequently marked by frustration, disengagement and dropout, because learners must simultaneously absorb an unfamiliar non-natural language, its syntax and semantics, and the abstract art of decomposing problems into algorithms. In many Nigerian institutions, instruction still relies on traditional lecturing, an approach the authors argue has done little to improve the learning experience. The team instead paired problem-based learning, a constructivist method in which students collaboratively analyse authentic problems before writing code, with structured access to Gemini.

The design was deliberately rigorous for a classroom setting. Because intact classes could not be randomly shuffled without disrupting academic schedules, the researchers used a non-equivalent control group pre-test–post-test design. Twenty-nine students received Gemini-enhanced problem-based learning while thirty-three followed the same problem-based sequence without AI support. Crucially, the control condition was not passive lecturing: both groups covered the same curriculum, completed equivalent tasks, received equal instructional time and were assessed with identical rubrics. The only meaningful difference was that the experimental group could consult Gemini as a scaffold, while the control group relied on course materials, peer collaboration, manual debugging and instructor feedback.

Over eight weeks, the intervention followed a carefully scripted progression. Week one established baselines using a thirty-one-item Multi-Dimensional Measure of Trust and a thirty-five-item computer programming achievement test. Subsequent weeks moved from basic syntax through algorithm design and pseudocode, data structures, modular functions and a regression computation, culminating in a collaborative capstone project. In the experimental group, Gemini was introduced only after students had attempted to interpret each problem and draft an initial solution, a rule designed to prevent the chatbot from replacing independent reasoning. Students then used the tool to explain unfamiliar syntax, critique algorithms, diagnose errors, generate test cases and review function structures, recording whether each suggestion was accepted, modified or rejected. Gemini never completed graded work or assigned marks.

The quantitative results were striking. On the pre-test, the two groups were statistically indistinguishable, with means of roughly twenty-three and twenty-four points. By the post-test, the Gemini-enhanced group averaged 73.90 against 57.37 for the control group, a difference that produced an effect size of nearly 0.79 in the analysis of variance. Because variance differed between groups, the team supplemented conventional analysis of covariance with HC3 robust estimation and bias-corrected bootstrapping. The adjusted mean difference held at 16.42 points with a 95 percent confidence interval of 14.16 to 18.67, confirming that the achievement gap was not an artefact of unequal spread or influential outliers.

Trust told a parallel story. Measured across four dimensions drawn from McKnight’s framework, performance trust, moral trust, ethical trust and transparency, trust scores climbed from around 1.6 on a five-point scale at baseline to above 4.2 in the Gemini group, while the control group rose only to the mid-2s. The analysis of covariance confirmed that instructional condition, not gender, drove the difference: gender itself and the condition-by-gender interaction were both statistically nonsignificant. That finding matters in a context where women remain under-represented in computing and where scepticism about technology adoption is often framed through a gendered lens. Here, female students in the AI-supported group actually posted the largest trust gains, rising 2.68 points on average compared with 1.41 for their counterparts without AI.

To understand why the intervention worked, the researchers interviewed eight experimental-group students, four men and four women, using inductive thematic coding. Participants described Gemini as valuable across the full arc of programming work: clarifying problems, developing algorithms, debugging code and enabling self-directed learning. One interviewee linked the approach to improved problem-solving, algorithm skills and structured program design; another said it motivated engagement in coding classes and described natural-language interaction as increasing their enthusiasm for coding. Others associated trust with transparent prompts, responsible data handling and clearly defined lines of responsibility between what the AI generates and what is expected of the student, suggesting that trust coexisted with human oversight rather than blind acceptance.

The authors ground these findings in an unusual theoretical pairing, combining Bourdieu’s concept of habitus, the idea that dispositions are formed through social and educational experience, with McKnight’s technology-specific trust framework. Their interpretation is that trust is not merely a pre-existing personality trait but something that can be cultivated through structured pedagogical encounters with AI. When instructors explained how Gemini worked, how prompts shaped outputs and how data were protected, students appeared to calibrate their reliance on the tool rather than either rejecting it or deferring to it uncritically. The nonsignificant gender effects further suggest that socially formed dispositions did not uniformly determine outcomes in this sample.

The researchers are candid about the limits of their evidence. Intact classes, non-random assignment, different institutions and facilitators, a small qualitative sample of volunteers, and self-reported trust all constrain causal claims, and the study does not establish that problem-based learning is superior to lecturing or that Gemini alone produced the gains. All four quoted interviewees were favourable, and one closed-ended ethical concern went unexplored. Still, for educators and policymakers weighing whether generative AI belongs in formal curricula, the study offers a compelling template: embed the tool inside active, problem-centred pedagogy, require students to reason first, verify AI output against course materials, and make transparency and accountability explicit. Under those conditions, the authors conclude, carefully structured AI support may meaningfully complement programming education, and trust in the machine may be something teachers can actually teach.

Subject of Research: The effect of AI-integrated problem-based learning on programming achievement and student trust in artificial intelligence in higher education

Article Title: Exploring trust in artificial intelligence in a problem-based computer programming course

Article References: Omeh, C. B., Ezeamuzie, N. O., & Olelewe, C. J. (2026). Exploring trust in artificial intelligence in a problem-based computer programming course. Discover Education, 5(1), Article 1172. https://doi.org/10.1007/s44217-026-02200-7

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02200-7

Keywords: generative AI, Google Gemini, trust in AI, problem-based learning, programming education, computer science education, quasi-experimental study, Nigeria, higher education, gender differences, educational technology, achievement

The research team surveyed 499 information technology employees working in AI-enabled organizations in Hyderabad, India, one of the country’s most dynamic technology hubs. Using a cross-sectional survey design, the researchers measured how regularly employees used AI tools in their daily tasks, how much stress those technologies generated, how much they trusted AI systems to perform work-related duties reliably, and how psychologically well they felt while working. All constructs were captured on seven-point Likert scales, with items adapted from established measurement traditions, including Davis’s foundational work on technology acceptance, Tarafdar and colleagues’ technostress framework, McKnight’s trust-in-technology scales, and Zheng and colleagues’ well-being measures.

To make sense of the data, the team turned to three complementary theoretical pillars. The Job Demands–Resources model provided the scaffolding for understanding how workplace characteristics can act as either demands that deplete employees or resources that energize them. The Unified Theory of Acceptance and Use of Technology 2, known as UTAUT2, helped explain the motivational drivers behind AI adoption itself. Social Exchange Theory rounded out the framework by framing the relationship between employees and the intelligent systems they work alongside as a kind of reciprocal exchange, in which trust functions as the currency of cooperation.

Methodologically, the study was rigorous by the standards of organizational psychology. The researchers first ran exploratory factor analysis to uncover the underlying structure of their measures, then confirmatory factor analysis to verify that each construct held together as intended. Finally, they employed covariance-based structural equation modeling, using IBM SPSS 29 and AMOS 28, to test the full network of hypothesized relationships simultaneously. Bootstrapping procedures were used to assess mediation, a technique that repeatedly resamples the data to estimate how confidently an indirect effect can be claimed. The study protocol was reviewed and approved by an Institutional Ethics Committee, with formal exemption approval granted on 6 May 2026, and all participants provided electronic informed consent before completing the anonymous online questionnaire.

The headline result is deceptively simple: AI adoption has a positive direct impact on employees’ psychological well-being. Workers who regularly use AI tools to perform their tasks report feeling psychologically better at work, a finding consistent with the growing body of evidence that automation and augmentation can relieve employees of drudgery, sharpen decision-making, and make work feel more meaningful. But the study did not stop at that direct effect. It also found that AI adoption increases trust in AI and, at the same time, increases technostress, the technology-induced strain that arises when systems demand constant learning, accelerate workloads, blur boundaries, and create fears of obsolescence.

Here is where the story becomes more complicated and more interesting. Technostress, measured with items such as whether using AI technologies increases one’s workload, negatively impacts employees’ psychological well-being. Trust in AI, captured by items asking whether employees consider AI systems reliable in performing work-related tasks, positively impacts well-being. In other words, AI adoption sends two opposing signals through the workforce simultaneously. One pathway transmits the negative effects of new technology through heightened stress, while the other transmits positive effects through the confidence employees place in the systems they use. The net experience of any given employee is the sum of these competing currents.

The most consequential finding, however, concerns mediation. The researchers hypothesized that technostress would negatively mediate the relationship between AI adoption and well-being, transmitting harm through increased technology-related stress, while trust in AI would positively mediate the same relationship by fostering confidence in AI systems that is itself associated with psychological well-being. When the bootstrapping analysis was complete, the results showed something even stronger than partial mediation: technostress and trust in AI fully mediate the relationship between AI adoption and employee well-being. In statistical terms, this means that AI adoption does not influence well-being through some mysterious direct residue once these two mechanisms are accounted for. Its entire psychological footprint travels through the twin channels of stress and trust.

That full-mediation result carries a practical punch for organizations racing to embed AI into their operations. If the well-being consequences of AI adoption flow entirely through technostress and trust, then the levers that matter most are not the technologies themselves but the human conditions surrounding them. Companies that roll out AI without managing workload pressures, skill anxiety, and constant connectivity may find that the stress pathway dominates, eroding the very well-being that productivity gains were supposed to enhance. Conversely, organizations that invest in transparency, reliability, and demonstrable competence of their AI systems can strengthen the trust pathway, converting adoption into a genuine psychological resource. The study’s authors frame this as a call for human-centered AI practices and responsible AI governance within organizations, positioning employee psychology as a central concern of AI strategy rather than an afterthought.

The research also fills a notable gap in the literature. While past studies have demonstrated positive effects of AI on organizational productivity and efficiency, the impact of AI adoption on employees’ psychological well-being has remained unclear, with most work examining either the positive or the negative pathway in isolation. By modeling both the dark side and the bright side of AI adoption within a single study, the researchers provide a more complete map of how intelligent technology reshapes the inner lives of workers. The finding that AI adoption simultaneously raises technostress and builds trust captures the ambivalence many employees report feeling: energized by capable tools, yet unsettled by the pace of change they impose.

Certain limitations are worth keeping in view. The study is cross-sectional, meaning it captures a single moment in time and cannot definitively establish causal direction; it is theoretically possible, for example, that employees who feel psychologically well are simply more inclined to adopt and trust AI. The sample was drawn from IT employees in a single Indian city, a population that is both unusually familiar with technology and embedded in a rapidly transforming labor market, so generalizing to other industries, cultures, or occupational groups requires caution. The authors also note that the article was shared early as a peer-reviewed, accepted version subject to further edits before the final Version of Record. Still, the core message stands out with unusual clarity for workplace research: the psychological consequences of AI are not written into the algorithms. They are written into the stress employees feel and the trust they extend, and both of those are things organizations can actively shape.

Subject of Research: The mediating roles of technostress and trust in AI in the relationship between workplace AI adoption and employee psychological well-being

Article Title: Technostress and trust in AI mediate the association between AI adoption and employee well-being

Article References: Prasad, K. D. V., Srinivas, V., Singh, S., Kothari, H., Nag, D., & Pathak, A. (2026). Technostress and trust in AI mediate the association between AI adoption and employee well-being. Discover Psychology. https://doi.org/10.1007/s44202-026-00928-9

Image Credits: AI Generated

DOI: 10.1007/s44202-026-00928-9

Keywords: AI adoption, technostress, trust in AI, employee well-being, Job Demands-Resources model, UTAUT2, Social Exchange Theory, structural equation modeling, organizational psychology, IT employees, human-centered AI, responsible AI governance

The numbers tell a contradictory story. In the self-selected sample, 82.5 percent of respondents reported using LLMs in teaching and teaching-related activities, and they rated the benefits of these tools highly, with a mean score of 3.93 out of 5. They were even more convinced that their students use AI regularly, scoring that belief at 4.19, the highest mean in the entire survey. Yet trust in LLM outputs scored just 2.45 out of 5. Privacy concerns registered at 3.82, and confidence in distinguishing student-written work from machine-generated text sat at a meager 2.58. The lowest score of all, 1.99, went to using LLMs for grading student work — a result the authors read as deliberate professional caution in the highest-stakes corner of academic practice.

What makes this pattern genuinely paradoxical, the researchers argue, is that it defies a well-established expectation from the international literature. Studies of college students, including large-scale work on ChatGPT attitudes, have consistently found that familiarity breeds comfort: the more students use AI tools, the more positive their attitudes and the stronger their intent to use them responsibly. Among educators, that relationship collapses. An exploratory correlation between LLM familiarity and trust in this sample was effectively zero, and years of experience showed a weak negative association with trust, tentatively suggesting that the most seasoned educators trust the tools somewhat less, not more.

The authors attribute this divergence to position and accountability. Students often treat LLMs as a shortcut to finished work; educators must answer for assessment integrity, teaching quality, and the credibility of the qualifications their institutions award. That responsibility makes them acutely alert to what the tools get wrong. The Nordic setting may amplify the effect rather than dampen it. In systems built on high institutional trust, academic freedom, and student autonomy, a technology that muddles verification and accountability may land harder than in systems already accustomed to surveillance-based quality checks. Trust, the study suggests, does not come from knowing the tools better — it comes from institutional structures that make verification, accountability, and recourse real.

Underpinning the paradox is what the researchers call institutional uncertainty: educators routinely using a technology they do not trust, without adequate policies, governance structures, or assessment frameworks to manage the risks. The survey itself did not directly measure institutional governance, and the authors are careful to flag their governance-failure interpretation as an inference. But it is one anchored in external evidence. Multi-country Nordic policy studies have found institutional responses ranging from comprehensive AI guidelines at one end to little more than a reference to existing integrity norms at the other, with nothing specific about LLMs. In practice, each educator ends up drawing the ethical line alone.

The study models the paradox as a self-reinforcing six-stage loop. It begins with perceived necessity — students are already using these tools, so opting out feels unrealistic — which drives high adoption. Adoption brings repeated exposure to outputs that are unreliable or hard to verify, which coexists with low trust. Low trust, in turn, coincides with the absence of clear institutional rules, and that absence pushes educators back onto personal judgment, normalizing further use and restarting the cycle. The authors stress that this is a conceptual synthesis of co-occurring patterns, not a statistically tested causal chain, and they present it as hypothesis-generating. But if it holds, the implication is sobering: adoption alone will never build trust, and the plausible points of intervention are the policy and uncertainty gaps at the end of the loop.

Nowhere is the governance gap more visible than in assessment. Nearly half of respondents — 47.5 percent — named academic integrity as the foremost challenge of AI integration, and the clustering of that concern with adapting assessment methods (38.8 percent) and maintaining critical thinking (40.0 percent) suggests these are experienced as interconnected systemic problems rather than isolated disciplinary issues. Respondents reported that students rarely reveal their LLM use, and their own confidence in detecting AI-assisted work was low. The problem, the authors argue, is not simply that students may misuse the tools; it is that the assessment infrastructure lacks the capacity to function meaningfully when the boundary between human and machine-generated work has blurred. Recent reviews support this shift in framing, moving from generative AI as a cheating tool to generative AI as a systemic challenge demanding redesigned assessment and institutional policy.

Regulation is beginning to catch up, at least on paper. Under the EU AI Act, AI systems used to determine access to educational institutions, evaluate learning outcomes, or assess appropriate levels of education are classified as high-risk, triggering requirements for transparency, human oversight, and conformity assessment. The near-total avoidance of LLMs for grading in this sample may partly reflect an intuitive recognition of those stakes. But the broader pattern of informal, ungoverned adoption across other academic activities suggests most institutions have not yet built the governance infrastructure that emerging regulatory expectations will demand. The authors also point to the gap between perceived competence and actual trust — respondents rated LLMs moderately competent in their own fields at 3.01, yet trusted outputs at only 2.45 — as evidence that trust depends on governance, transparency, and institutional backing rather than technical performance alone.

Interestingly, educators are not waiting for permission to rethink pedagogy. When asked which skills matter most in an AI-enhanced environment, they ranked critical thinking first, followed by information literacy and ethical reasoning — priorities that read as the constituent elements of what the broader literature calls AI literacy. Their favored teaching approaches, project-based and collaborative learning, point toward process-oriented methods in which the production of text alone is no longer a reliable indicator of student competence. Yet this pragmatic pedagogical reasoning is happening without coordination: individual educators are independently arriving at similar conclusions, without shared frameworks or institutional support.

The study’s limitations deserve emphasis. The sample of 80 is modest, recruited through open channels that likely over-represent educators already interested in AI, making the 82.5 percent adoption figure an upper bound rather than a prevalence estimate. The sample skews toward Finland and Norway, toward experienced staff with doctoral degrees, and toward the social sciences and humanities. A descriptive comparison showed Nordic and non-Nordic respondents reported near-identical trust levels, suggesting the pattern is not a Nordic artifact, but generalizability remains limited. Still, the consistency of the pattern across every section of the survey makes it hard to dismiss. The authors’ conclusion is a call for structural reform rather than behavioral adjustment: explicit institutional strategies clarifying acceptable use, responsibility, and accountability, developed with educators and students rather than imposed as top-down compliance. Trustworthy AI in education, they argue, is not a property of the technology but a socio-technical achievement that emerges from governance, transparency, and shared understanding — and right now, the institutions that certify human knowledge are leaving that achievement to individuals.

Subject of Research: The adoption–trust paradox in large language model use among higher education professionals and its implications for institutional AI governance

Article Title: Using LLMs without trust: the adoption–trust paradox as institutional governance failure in academic ecosystems

Article References: Kringen, P., Gallucci, A., Hildt, E., Johansen, F. R., Westerlund, M., & Tigerstedt, C. (2026). Using LLMs without trust: the adoption–trust paradox as institutional governance failure in academic ecosystems. AI &amp; SOCIETY. https://doi.org/10.1007/s00146-026-03323-z

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03323-z

Keywords: large language models, higher education, academic integrity, institutional governance, trust in AI, adoption–trust paradox, assessment, Nordic higher education, EU AI Act, AI literacy, educator survey, AI & Society