The proposal responds to a limitation the authors identify in discussions about artificial intelligence, which tend to organize progress around cognition and the projected arrival of superintelligence. Emotional intelligence has been treated as a separate human capacity, while modern affective-computing systems already combine text, audio and video to infer feelings, and large language models are increasingly tested on emotional reasoning. Commercial voice assistants can also modulate their delivery in response to a user’s vocal affect. These developments show that affect-sensitive machines are being built, but the perspective notes that their performance is usually measured against human ceilings rather than asked to surpass them. Several relevant benchmarks and product claims cited by the researchers remain preprints, commercial reports or press material rather than independent evidence of clinical benefit.

Pilot results illustrate both the promise and the limits of current work. One framework combines voice, facial expression, physiology and behavior to detect stress, reporting 85% accuracy in a small study using induced stress alongside favorable ratings of therapeutic alliance from participants. A binary stress classifier, however, does not demonstrate general emotion understanding, and therapeutic-alliance ratings show only how users perceived the interaction, not whether it improved their condition. In a separate 200-participant experiment, supplying facial-expression context changed responses from a multimodal language model and increased ratings of perceived empathy. That result cannot establish that the system accurately tracked the user’s state or that the exchange was beneficial. Earlier experiments showed that skin conductance and electromyography could guide an animated agent’s empathic feedback, but those systems were rule-based, used narrow channels and did not maintain a model of the user.

The proposed route to greater perceptual accuracy is to combine conventional cues such as face, voice and words with peripheral or contactless signals that humans cannot directly perceive in one another. One possibility is electromyography from small muscles around the ear, long considered vestigial but now associated in laboratory studies with directional auditory attention, listening effort and responses to positively or negatively arousing stimuli. These signals operate in the microvolt range and must be separated from larger sources such as speech, jaw movement, chewing, swallowing, blinks, head motion, facial expressions and electrode motion. Nearby electrodes, local referencing, spatial filtering, co-registered inertial measurements and separate signals for jaw and facial activity could help suppress those artifacts. The usefulness of the residual signal outside carefully controlled recordings remains an empirical question requiring tests across held-out devices and settings.

A second channel is contactless optical measurement. Computer-vision methods can magnify minute facial movements, track sweat-related changes in skin luminance and estimate heart rate or heart-rate variability from video. Speech analysis provides a further source of information, with prosody generally providing stronger evidence of arousal than of emotional valence. None of these channels directly measures an emotion. Electrodermal activity can reflect excitement, embarrassment, effort, novelty or temperature; heart rate varies with breathing, posture and fitness; facial and auricular muscle activity can be shaped by display rules, attention and stimulus features. Meta-analyses cited in the perspective have found no dependable autonomic fingerprint for discrete emotion categories, and facial movements are not reliable readouts of inner emotional states. A system would therefore need to combine ambiguous evidence with context and individual baselines while carrying uncertainty, rather than read an emotion label directly from a sensor.

The authors’ second major component is a generative model grounded in active inference. Rather than mapping facial features to a fixed emotion label, this model would infer the hidden causes of a person’s signals and predict how the person’s state is likely to develop. It would forecast the trajectory of self-reported affect over subsequent conversational turns and estimate how that trajectory might change after different responses. Such a model would be tested against expert predictions and simpler baselines, including last-value and autoregressive forecasts, under the same scoring rules. The third component, functional allostatic self-regulation, would maintain bounded control variables such as uncertainty about the user, an intervention budget and running counts of prediction errors or constraint violations. These variables would gate behavior; removing them in ablation tests should increase false interventions or oscillations if the component is necessary. The terminology describes control functions rather than subjective experience.

Together, the elements form a proposed closed loop rather than a pipeline that merely inserts “the user seems stressed” into a prompt. A persistent state estimate would carry valence, arousal and attentional effort across turns with uncertainty attached. A transition model would include the effect of the system’s own utterance or device setting on the user. Candidate responses would then be scored over a short horizon, balancing outcomes requested by the user against gains in knowledge about the user’s state, and the best admissible action would be selected before the loop repeated. Online adaptation would update the transition model and policy from observed effects. Hard constraints would bound actions, prohibit specified behaviors and preserve two privileged options: abstaining and escalating to a human. A user could stop the loop at any time.

The authors argue that the computational demand might be manageable because the state space would contain only a few affective variables, plans would cover a few turns and candidate responses would be proposed by a language model acting as the loop’s actuator rather than its estimator. High-rate state estimation could run as Bayesian filtering on a wearable, plan scoring could occur once per conversational turn, and model adaptation could occur once per session. The language model need not fit entirely on the device. This separation remains a design proposition, however: real-time active inference of the described scale has been demonstrated in robots and simulated agents, but the complete integrated system has not been built or benchmarked as the perspective specifies.

The same design could become a powerful instrument of persuasion if its objective were wrong. A system optimized for engagement, for example, might learn to sustain arousal, whereas one optimizing reduced listening effort could adjust a hearing aid toward a companion’s voice, increase noise reduction and shorten its own contributions. Feedback does not automatically correct mistakes: biased sensing can produce biased corrections, and success measured only through the sensors used for control may conceal harmful effects. The authors therefore require observable state variables, calibrated uncertainty, bounded loop gain, independent outcome measures, limited interventions and continuous human oversight with the capacity to override the system. They say steerability comes from a user-set objective and enforceable constraints, not from superior perception itself. Perception that improves emotional regulation can equally make manipulation more effective if the target is engagement or commercial value rather than the user’s wellbeing.

Testing the proposal would require preregistered comparisons with expert decoders and skilled counselors using the same recordings, tasks and outcomes. At the measurement level, peripheral and contactless channels would need to carry reproducible, stimulus-locked information at sub-second scales. At the state level, adding those channels to a face-and-voice baseline would need to improve calibrated prediction of a person’s concurrent report across held-out individuals, cultures and devices and exceed expert performance by a predefined margin. A second prediction says the complete closed loop would lower independently measured effort or distress compared with prompt conditioning using identical sensors and language models. A third says removing the internal regulatory variables would increase false interventions or interactional oscillation. Naturalistic longitudinal studies, sensor ablations, missing-modality tests, uncertainty calibration, false-intervention rates and short- and long-term outcomes would be essential. Apparent emotion and perceived empathy cannot serve as ground truth.

Governance is treated as part of the architecture rather than a policy added afterward. The perspective calls for explicit, channel-specific and revocable consent, visible hardware indicators for cameras and microphones, on-device status for wearables, on-device processing by default, limited retention, deletion on request, access controls and an audit log of interventions. The affective target would be set by the user, or by a clinician with consent, and could be rejected at any time; engagement and commercial objectives would not enter that target. Even with these controls, risks would remain. Optical and electrode-based measurements can be affected by skin tone, illumination, hair type, medication, age, fitness and other individual differences, while training priors may encode group stereotypes. The central claim is therefore not that a machine will soon acquire emotional superpowers, but that AI development is already creating systems able to infer and influence affect. Whether their greater sensitivity serves human autonomy or becomes a more precise means of control, the authors argue, will be determined by the objectives and constraints built into their closed loops.

Subject of Research: A proposed framework for testing artificial systems that perceive, model and regulate human affect.

Article Title: The rise of super-emotional intelligence

Article References: Bonnaire, S., Corona-Strauss, F. I., & Strauss, D. J. (2026). The rise of super-emotional intelligence. Frontiers in Artificial Intelligence, 9, Article 1975692. https://doi.org/10.3389/frai.2026.1975692

Image Credits: AI Generated

DOI: 10.3389/frai.2026.1975692

Keywords: super-emotional intelligence, affective computing, emotion recognition, active inference, psychophysiology, human-centered AI, AI governance, closed-loop systems, neurotechnology, manipulation risks, rise, super-emotional

The study, conducted by Mireille Riachi of the University of Balamand, surveyed 28 in-service Lebanese schoolteachers drawn from public and private schools across the country. Every participant had at least five years of classroom experience, and the sample was deliberately varied across school sector, educational level, subject specialization, and region. Data came from an open-ended online questionnaire structured around four dimensions: teachers’ understanding of AI, their actual uses of it, their professional development needs and institutional barriers, and their ethical concerns about data privacy, algorithmic bias, and the preservation of human-centered teaching. Responses were analyzed through thematic analysis, with a subset of coded data independently reviewed to check consistency.

The headline numbers reveal a striking paradox. Eighty-two percent of the teachers reported being familiar with AI — they had read about it, encountered chatbots, or used tools labeled as AI-driven. Yet only 29 percent had actually integrated AI into their instructional practices, and most of those confined it to administrative chores: automated grading, timetabling, generating standardized feedback. Just 11 percent had ever received any formal training related to AI in education. In other words, awareness has raced far ahead of capability, leaving a wide gap between what teachers know exists and what they can competently deploy in service of learning.

That gap, the study argues, is not merely a matter of individual skill deficits. Drawing on the Technological Pedagogical Content Knowledge framework — the influential model holding that effective technology integration emerges from the interplay of pedagogical, content, and technological knowledge rather than technical proficiency alone — Riachi interprets the pattern as a structural misalignment. Teachers are being exposed to AI tools without the pedagogical scaffolding needed to align those tools with instructional goals. The result is a kind of low-risk, peripheral engagement: AI handles the paperwork while the actual craft of teaching remains untouched by it.

The institutional barriers teachers described were concrete and, in the Lebanese context, severe. Public school teachers in particular pointed to poor internet connectivity, insufficient classroom devices, and the absence of technical support staff. One participant captured the frustration bluntly: “We are told to innovate, but no one tells us how or gives us tools.” Ninety-three percent of respondents identified AI-specific training as a pressing need, and many insisted that such training must be anchored in pedagogical frameworks rather than reduced to software tutorials. The study situates these complaints within Lebanon’s broader digital education landscape, where national strategies exist on paper — including a National Artificial Intelligence Strategy and large-scale teacher training initiatives that reached over 16,400 educators between 2020 and 2021 — but where implementation remains fragmented and unevenly distributed between well-resourced private institutions and strained public ones.

From the narratives, three distinct orientations emerged. The first, labeled the Hesitant orientation, describes teachers with high awareness but low engagement: they acknowledge AI’s relevance but refrain from using it pedagogically, deterred by lack of training, fear of error, and anxiety about undermining student learning. “I keep hearing about AI, but I’ve never had a real session or training that shows me how it works in class,” one wrote. Another confessed, “It’s like we’re expected to perform magic without tools.” These teachers often reported feeling left behind by institutional digital transitions, their digital literacy confined to routine administrative tasks.

The second orientation, the Adopters, stand at the cutting edge of classroom experimentation. Often self-taught, they deploy tools like ChatGPT, adaptive quiz platforms, and speech-to-text software to personalize learning, support language development, and enrich formative assessment. “I use AI to tailor questions based on student levels — it saves time and improves engagement,” explained a French teacher at a private school. Another reported that ChatGPT helped a shy student write his first full essay. These educators embody what researchers call the “teacher as designer” paradigm, actively reinterpreting technological possibilities to serve pedagogical ends. Yet even they operate without institutional recognition or structured frameworks, relying on personal initiative in an environment that offers validation to almost no one.

The third orientation may be the most provocative: the Skeptics, who resist AI on philosophical, ethical, and pedagogical grounds. Their objections are not technophobia but a considered normative vision of what education is for. “What happens when a child is wrongly flagged by AI? Do we blindly trust the machine?” one asked. “I teach literature. No machine can replace the human connection that fosters discussion and critical thought,” insisted another. A Beirut public school teacher voiced a data privacy anxiety that echoes worldwide: “I don’t know where the data goes, and that worries me. I wouldn’t want student information used by third parties.” Others worried about the dehumanization of teaching itself — “AI may be smart, but it’s not humane. Students need empathy, not algorithms.”

Crucially, the study refuses to read these ethical objections as mere resistance to progress. Instead, Riachi frames them as evidence of professional reflexivity — teachers acting as ethical mediators rather than passive consumers of technology. The skeptics’ concerns about algorithmic bias, surveillance, and student autonomy align closely with international guidance from UNESCO and the OECD, both of which advocate human-centered approaches to AI in education. The study suggests that skeptics should be treated as ethical interlocutors, deliberately included in professional learning communities and policy discussions, because a technology rollout that silences its most thoughtful critics is a rollout primed to repeat their predicted mistakes.

The recommendations that follow are pointed. Professional development must move beyond tool-based training toward pedagogical design, critical digital literacy, and ethical reasoning — with differentiated pathways for each orientation: scaffolded entry points and mentoring for the hesitant, collaborative co-design spaces for adopters, and seats at the ethical policy table for skeptics. Institutions must supply the infrastructure, leadership, and curricular flexibility that make innovation a collective, supported process rather than an individual burden. And robust ethical frameworks covering data privacy, algorithmic bias, accountability, and student autonomy must be formulated and disseminated before, not after, AI becomes embedded in schooling. The deeper message travels well beyond Lebanon: sustainable AI integration in education depends on repositioning teachers not as subordinate technicians but as co-designers of learning, whose professional judgment — not the technology itself — determines whether intelligent machines strengthen or erode the fundamentally human enterprise of teaching.

Subject of Research: Teachers' orientations toward artificial intelligence integration in Lebanese schools

Article Title: A qualitative typology of teachers’ orientations toward artificial intelligence integration in Lebanese schools

Article References: Riachi, M. (2026). A qualitative typology of teachers’ orientations toward artificial intelligence integration in Lebanese schools. Discover Education, 5(1), Article 1185. https://doi.org/10.1007/s44217-026-02264-5

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02264-5

Keywords: artificial intelligence, education, Lebanon, teachers, qualitative research, TPACK framework, professional development, data privacy, algorithmic bias, educational technology, Discover Education, human-centered AI

The article, authored by Layli Navruz-zoda of Bukhara State University and colleagues at institutions across Uzbekistan, does not present new experimental data. Instead, it offers something arguably more consequential for the field: a carefully constructed conceptual framework, the Human-Centered AI Adoption Framework, that stitches together two research literatures which have largely evolved in isolation. One literature, rooted in the dynamic capabilities tradition, shows that internal digital and dynamic capabilities predict AI adoption and performance more strongly than external support. The other, drawn from human-centered and responsible AI research, specifies what a workable human-AI configuration looks like. What has been missing, the authors contend, is the junction between them, along with the methodological consequences that follow.

The framework rests on a deliberately restricted set of four human-centered principles: meaningful human control, transparency and explainability, augmentation where appropriate, and trust and accountability. The authors are explicit that this is a subset, not a comprehensive charter. Each principle earned its place through a retention rule requiring both recurrence across the human-centered AI literature and a stated mechanism linking it to continued use at small-firm scale. The logic is pragmatic: in a small enterprise, a tool that staff cannot understand or override is simply abandoned rather than formally contested. Broader requirements such as fairness, privacy, worker participation, safety, limits on surveillance and accessibility enter the framework as a cross-cutting safeguards dimension, acknowledged as normative design requirements whose SME-scale mechanisms remain unspecified.

At the technical heart of the framework lies a distinction that sounds subtle but carries real analytical weight: the difference between reconfiguring and adaptation. Reconfiguring is an activity, the work of redesigning roles, procedures and decision rights around a new tool. Adaptation is a state, the realized configuration of formal arrangements, shared norms and skills that the activity produces. The correspondence between the two is imperfect. Firms attempt redesigns that revert, procedures get rewritten but not followed, training is delivered while roles remain unchanged. That residual variance, the authors argue, is not statistical noise but the very location of symbolic use, the phenomenon where tool ownership is recorded while routines stay frozen.

This distinction underpins the article’s six propositions, organized under two objectives. The first objective concerns how human-centered design conditions the conversion of AI access into capability. Proposition P1.1 holds that a human-centered foundation raises the likelihood that sensing, seizing and reconfiguring occur, the classic microfoundations of dynamic capabilities. Proposition P1.2 predicts that where the foundation is absent, adoption stalls at symbolic use. Proposition P1.3 rejects the assumption that AI is uniformly augmentative, arguing instead that the appropriate configuration depends on task criticality, error cost, employee expertise, regulatory constraint and system type; full automation remains appropriate for some routine, low-stakes, high-volume tasks.

The second objective addresses how organizational adaptation converts capability into competitiveness. Proposition P2.1 asserts that reconfiguring produces structural, cultural and human-capital adaptation as a realized state downstream of it, and that the two are empirically separable. Proposition P2.2 goes further: adaptation carries a substantial part of the association between AI capability and competitiveness, so controlling for it should attenuate, though not necessarily eliminate, the direct link between tool ownership and performance. Proposition P2.3 proposes a temporal ordering in which competitiveness dimensions realize in sequence, with efficiency emerging earliest because it follows from task-level substitution once use is embedded, responsiveness and innovation arriving later, and realized resilience observable only across an actual disruption.

What elevates the article beyond conceptual synthesis is its methodological rigor. Each proposition is paired with candidate indicators and a research design capable of testing it. For P1.2, the authors suggest measuring the gap between tool ownership and the share of relevant transactions actually passing through the tool, using longitudinal panels with archival and log data. For P2.1, they propose comparing a one-factor measurement model against distinct but correlated factors, treating discriminant validity as a test of the claim rather than an assumption. For P2.2, they recommend three-wave panels with baselines, difference-in-differences designs around exogenous deployment timing, and matched comparisons to address the self-selection that plagues adoption research. The authors are candid that self-reported use cannot separate symbolic from embedded use, and that only behavioral and archival traces, such as system access logs and documented procedure revisions, can.

The framework’s engagement with emerging and transition economies adds a distinctive dimension often absent from AI adoption research. The authors note that public cloud AI compute is concentrated in a small number of jurisdictions, meaning most countries consume AI services hosted elsewhere, a structural constraint they expect to limit reconfiguring to what foreign platforms permit. Language compounds the challenge: model performance in lower-resource languages remains materially weaker than in English, and Uzbek is a documented low-resource case where basic annotated resources have only recently been constructed. The authors expect the transparency principle to bind harder in such settings, because staff cannot verify outputs they cannot fully read. They also flag that Uzbekistan’s regulatory framework addresses data localization and national AI capacity but does not yet specify obligations concerning algorithmic management or the contestation of automated decisions, meaning safeguards are least likely to be supplied by law and most likely to require deliberate design.

The practical implications are refreshingly concrete. Firms should conduct readiness assessments with staff rather than for them, treating the proportion of employees able to explain what a tool does as a deployment gate. They should define per-tool success criteria before piloting, including a stated override rate at which the tool is reviewed. And they should schedule an explicit reconfiguring step, revising written procedures and role descriptions within a defined period after go-live, treating the absence of such revision as the diagnostic signal that symbolic use has taken hold. Which measure binds first depends on context: in a small manufacturer deploying predictive maintenance, the override measure matters most because unexplained recommendations get disregarded; in a professional-services firm using generative drafting, procedure revision matters more because output is easy to adopt informally but hard to govern.

The authors acknowledge four limitations with unusual candor: the framework is conceptual and untested, its human-centered layer is deliberately restricted, its constructs derive largely from high-income settings, and its indicators are unvalidated. Each limitation comes paired with a remedy, from multi-wave designs to comparative work in transition economies. Perhaps the most consequential extension they identify is the inclusion of customer and community effects, which are currently absent from the framework entirely. Worker-level outcomes such as autonomy, workload, skill use and job quality, by contrast, appear as ends in their own right, capable of diverging from firm competitiveness rather than tracking it. The article’s closing message is deceptively simple: an AI purchase is the beginning of a change project rather than the end of a decision, and researchers should follow firms past the point of purchase. Whether the framework’s propositions survive empirical testing remains an open question, but the authors have done the harder preliminary work of specifying exactly what would have to be observed for the connection between human-centered design and small-firm competitiveness to hold.

Subject of Research: A conceptual framework linking human-centered AI design principles to capability formation and organizational adaptation in small and medium-sized enterprises

Article Title: Human-centered AI adoption for small and medium-sized enterprise competitiveness: a methodological perspective on digital capabilities and organizational adaptation

Article References: Navruz-zoda, L., Najmiddinov, S., Norboev, S., Ibadullaev, E., & Zakirov, M. T. (2026). Human-centered AI adoption for small and medium-sized enterprise competitiveness: a methodological perspective on digital capabilities and organizational adaptation. Frontiers in Human Dynamics, 8, Article 1939008. https://doi.org/10.3389/fhumd.2026.1939008

Image Credits: AI Generated

DOI: 10.3389/fhumd.2026.1939008

Keywords: artificial intelligence, SMEs, human-centered AI, dynamic capabilities, organizational adaptation, digital transformation, AI adoption, competitiveness, responsible AI, transition economies, work design, research methodology

Human Digital Twins have emerged as a powerful paradigm in industry and healthcare, typically built on musculoskeletal models, physiological monitoring, and biomechanical simulation. These systems can predict body movement, muscle fatigue, workload, or injury risk with impressive fidelity. But the authors argue that such models are fundamentally incomplete for modern environments. Contemporary humans operate in complex multisensory settings characterized by multiple competing sensory streams, information overload, sensory conflict, and sensory strain. Think of a factory floor filled with machinery noise, visual clutter, and constant interruptions, or a virtual reality interface that generates conflicting visual and vestibular signals. These conditions place heavy demands on perceptual and attentional resources, and their effects on behavior cannot be captured by modeling physical characteristics alone.

Current neuroergonomic systems, the researchers note, remain largely descriptive rather than predictive. Many existing approaches analyze physiological signals or behavioral markers after the fact to infer cognitive states such as workload or stress. While valuable, these methods are limited in their ability to anticipate future cognitive states or support proactive adaptation. They treat cognition as a snapshot rather than a continuous, dynamic temporal process, and they often fail to generalize across tasks, individuals, and real-world conditions. Machine learning architectures such as Long Short-Term Memory networks and transformers can model temporal dependencies and enable forecasting, while reinforcement learning supports sequential decision-making, but the authors point out that these approaches are fundamentally passive and data-driven, relying on reward engineering and brute-force trial-and-error exploration. Crucially, none of them account for biological attention as it is understood in computational neuroscience, where attention operates as precision weighting, a mechanism that dynamically adjusts how much influence each sensory channel has on the brain’s internal model of the world.

The S-HDT framework is grounded in active inference, a theory from computational neuroscience that treats perception, cognition, and action as parts of a single unified process of probabilistic inference. Rather than passively receiving sensory data, an active inference system maintains a generative model of the world, continuously predicting what sensory input it should expect and updating its beliefs when predictions fail. The S-HDT applies this principle to model a human operator’s latent cognitive states, including cognitive load, attention, fatigue, emotion, and task engagement. These hidden states are inferred from multimodal observations, which may include physiological measures such as heart rate variability and muscular activity, behavioral cues, and environmental context. The model continuously minimizes the discrepancy between predicted and observed sensory data, producing progressively more accurate representations of the operator’s current cognitive state as an evolving probability distribution rather than a fixed label.

At the mathematical heart of the framework lies variational free energy, a quantity that serves as an upper bound on surprise, the unexpectedness of incoming observations. The free energy expression combines two terms: an expectation term that measures how well the surrogate twin’s current beliefs explain the observed data, essentially its prediction error, and an entropy term that reflects uncertainty about those beliefs. High entropy means several explanations remain possible, while low entropy indicates confidence. By minimizing free energy, the S-HDT approximates Bayesian inference over hidden cognitive states without needing to compute intractable posterior distributions exactly. Importantly, the authors emphasize that free-energy minimization should not be understood as minimizing environmental complexity itself. Surprise is relative to the model’s predictions and the operator’s current goals. In a crowded cocktail party scenario, the operator actively samples task-relevant information through attentional allocation, gaze, and head orientation, and the S-HDT maintains a suitable level of uncertainty for adaptive inference by continuously updating its internal belief distribution.

The framework also introduces an organizational distinction between core and mantle cognitive processes. Core processes correspond to evolutionarily conserved mechanisms that operate across most vertebrate species, including predictive modeling of sensory information, attentional allocation, action control, and threat detection. These provide the fundamental computational infrastructure that enables organisms to survive in uncertain environments by rapidly predicting and responding to external events. Mantle processes, by contrast, represent higher-order cognitive functions strongly shaped by learning, culture, and experience, such as goal management, social cognition, language processing, and symbolic reasoning. The authors stress that this distinction is a functional modeling abstraction rather than a strict anatomical or evolutionary dichotomy, intended to organize processes with different temporal and contextual characteristics while retaining their continuous, bidirectional interactions within the unified generative framework.

One of the most ambitious proposed capabilities is predictive cognitive simulation, in which a sufficiently validated implementation could generate plausible future trajectories of human cognitive states rather than merely estimating current ones. This matters because neuroergonomic environments benefit enormously from predicting performance degradation, workload surges, or attentional lapses before they become apparent in observable behavior. The framework performs continuous cognitive state estimation, constructs environmental mappings that relate task contexts and conditions to cognitive states, supports adaptive interfaces and task allocation strategies personalized to individual users, and functions as an intelligent decision-support system recommending interventions that minimize cognitive overload. The generative nature of the model also enables counterfactual reasoning, allowing the system to answer what-if scenarios by changing prior beliefs or sensory inputs within the generative model and propagating their effects forward, evaluating how changes in task difficulty or environmental stressors would affect physiological metrics without exposing human operators to unnecessary risk.

To illustrate the framework’s behavior, the team presented a proof-of-concept simulation comparing two neuroergonomic scenarios: a quiet environment with low external distraction and a cocktail party environment characterized by multiple competing sensory stimuli. The simulation deliberately instantiated only a restricted subset of the broader framework, focusing on auditory attention and perception-action dynamics, and did not model speech intelligibility, linguistic content, or informational masking. The auditory scenes were represented by 64-dimensional binaural spectral features, with spatial information encoded through left-right gain modulation and frequency-dependent spectral shaping. Sensory inputs passed through a hierarchical scale-space predictive processing model that calculated multi-level prediction errors, and the simulated agent interacted with the environment in a closed loop, updating its heading and attentional focus based on prediction error dynamics. Abrupt increases in free energy induced exogenous attentional shifts, simulating reflexive redistribution of cognitive resources under high load. Across all measured quantities, including free energy dynamics, attentional behavior, tracking performance, and cognitive effort, values were consistently larger in the cocktail party scenario than in the quiet environment, suggesting that rich stimulus environments involve greater uncertainty and prediction errors, requiring continual belief updating. The authors are careful to note that this simulation illustrates the behavior of selected framework components rather than providing empirical validation of human cognitive-state prediction.

The researchers are equally candid about the framework’s structural limitations. A fundamental challenge is the identifiability of latent cognitive states, since multiple internal states may produce similar observable physiological or behavioral patterns. Within active inference, the relationship between biosignals such as cardiac, electrodermal, or muscular activity and the latent affective, motivational, and attentional states they are meant to reveal is typically framed through a Markov blanket, a statistical boundary that renders internal states conditionally independent of the environment. But the authors caution that this boundary is defined relative to a chosen model, level of coarse-graining, and timescale, making it observer-dependent. The mapping from peripheral signals to internal states is many-to-one and not invertible, partly because autonomic signals are non-specific and, for affective states in particular, there are no fixed physiological signatures. Interoceptive inference accounts treat emotion not as a hidden variable passively lying behind the blanket but as a construction built from interoceptive signals together with contextual and conceptual priors, so the same peripheral pattern can support different affective interpretations. Physiological variables are also actively regulated through allostasis, acting simultaneously as evidence about and control over internal states, which makes the boundary partly circular. Active inference, the authors conclude, constrains the state identifiability problem rather than dissolving it, and the S-HDT therefore treats latent cognitive states as probabilistic surrogate representations whose uncertainty should be retained explicitly.

Practical challenges also remain in real-time multimodal data fusion, generalization across users with limited calibration data, and ethical deployment. Continuous inference of cognitive states raises issues of transparency, user autonomy, and data privacy, and the authors argue that predictive neuroergonomic systems must be designed with explainability and human-in-the-loop control, including communicating prediction uncertainty to users and requiring human confirmation before consequential adaptive interventions. Despite these hurdles, the S-HDT represents a conceptual step toward a new paradigm in neuroergonomics, one in which cognitive modeling is dynamic, predictive, and personalized rather than static and retrospective. By unifying perception, cognition, and action under a single computational principle and embedding prediction directly into the inference loop, the framework opens new opportunities for investigating neuroergonomic system design, human-centered artificial intelligence, and adaptive human-machine interaction, with future work focused on multimodal human-data validation and application-specific operationalization.

Subject of Research: A computational framework using active inference to model embodied human perception and cognition in digital twins for neuroergonomics

Article Title: Using a surrogate human digital twin to model embodied perception and cognition for neuroergonomics

Article References: Chowdhury, T. A., Francis, A. L., & Strauss, D. J. (2026). Using a surrogate human digital twin to model embodied perception and cognition for neuroergonomics. Frontiers in Neuroergonomics, 7, Article 1939412. https://doi.org/10.3389/fnrgo.2026.1939412

Image Credits: AI Generated

DOI: 10.3389/fnrgo.2026.1939412

Keywords: human digital twin, neuroergonomics, active inference, free energy principle, cognitive modeling, embodied cognition, multimodal sensing, attention, predictive simulation, human-machine interaction, surrogate model, human-centered AI

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 study, published in Discover Education, was conducted by Hossein Isaee and Hamed Barjesteh of Islamic Azad University in Iran and Neda Fatehi Rad of Islamic Azad University’s Kerman branch. It examined a question that has received surprisingly little empirical attention: what happens when two of the most talked-about trends in language education, artificial intelligence and positive psychology, are integrated within a single instructional design rather than studied separately? Most previous research, the authors note, has either celebrated the technical affordances of AI or documented the emotional benefits of strengths-based pedagogy, leaving the interaction between the two largely unexplored, especially outside Western and East Asian settings.

To fill that gap, the research team recruited 300 undergraduate English as a Foreign Language learners aged 18 to 24 from twelve universities across Iran, Oman, and Türkiye, with four institutions from each country. Twelve intact classes, each taught by a different instructor, were assigned through cluster randomization to one of three conditions: AI-supported instruction enriched with positive psychology activities, AI-supported instruction alone, or traditional exam-focused teaching. The intervention ran for ten weeks, with two 90-minute sessions per week, and was standardized through a common implementation booklet, a centralized training workshop in Tehran for lead instructors, and follow-up online coordination sessions across all sites.

The AI component relied on ChatGPT, used as a guided pedagogical support tool rather than an autonomous teacher. Learners practiced conversation, vocabulary, and writing with the system, receiving immediate feedback on their language production. In the integrated condition, these tasks were interleaved with positive psychology exercises grounded in Seligman’s PERMA model of well-being, which identifies positive emotion, engagement, relationships, meaning, and accomplishment as pillars of human flourishing. Students kept gratitude journals, wrote reflectively about their strengths, set goals, and discussed their progress in class, while teachers framed feedback not only around linguistic accuracy but around effort, persistence, and resilience. The theoretical logic drew on Fredrickson’s broaden-and-build theory, which holds that positive emotions expand attentional and cognitive resources, making learners more persistent, more willing to take communicative risks, and more engaged.

Quantitatively, the results favored the integrated approach across every major measure. The researchers assessed language proficiency with an institutional examination modeled on TOEFL- and IELTS-style tasks, with productive skills rated by trained raters who were blind to group assignment. Proficiency rose by nearly 20 points in the AI-plus-positive-psychology group, compared with roughly 12 points in the AI-only group and about 7 points in the control group. Analyses of covariance confirmed that the group differences remained significant after controlling for baseline performance, and the AI-only group also outperformed traditional instruction, indicating that the technology itself contributed something real even without the emotional layer.

The affective outcomes told a strikingly parallel story. Learners completed the Foreign Language Enjoyment Scale, the PERMA-Profiler short form for well-being, and the Academic Buoyancy Scale, all carefully translated and back-translated into Persian, Arabic, and Turkish before use. The integrated group showed the largest increases in enjoyment and well-being, and a particularly pronounced advantage in academic buoyancy, the capacity to bounce back from everyday academic setbacks such as exam stress and corrective feedback. A repeated-measures analysis showed that enjoyment grew over time in all groups but grew far more sharply where positive psychology activities were present, suggesting that AI alone delivers only a modest emotional benefit.

Perhaps the most theoretically consequential finding came from the mediation analysis. Foreign language enjoyment partially explained the relationship between instructional condition and language achievement: the integrated program boosted enjoyment, and higher enjoyment in turn predicted stronger proficiency outcomes. Because the direct effect remained significant, emotion did not account for everything, but the pattern supports the idea that enjoyment functions as a proximal affective mechanism linking instructional design to achievement, rather than being a mere byproduct of successful learning. In exam-driven systems where language learning is often associated with pressure and anxiety, that mechanism may matter more than anywhere else.

The qualitative strand of the study, based on interviews with 27 students and all 12 instructors, weekly teacher journals, and classroom observations coded inductively with intercoder agreement above 85 percent, illuminated why the intervention worked. Three themes dominated. First, learners described the AI system as a supportive, patient, and nonjudgmental coach: one Omani student said the system corrected her calmly and that she stopped fearing mistakes, while an instructor in Türkiye reported that normally quiet students began participating once the fear of public correction was lifted. Second, positive psychology activities reframed the emotional climate of the classroom, with learners saying they began noticing small improvements instead of fixating on errors and looking forward to sessions rather than dreading them. Third, cultural adaptation and teacher mediation proved central: students initially found some reflection activities unfamiliar, even questioning whether they counted as serious academic work, and grew receptive only when teachers connected the tasks to locally meaningful values such as perseverance, self-improvement, and communication.

The authors are careful about causal language. Because intact classes rather than individuals were assigned to conditions, and each class had a single instructor who could not be rotated, the design is a cluster-based quasi-experiment, and the findings are framed as evidence of association rather than definitive causal proof. The AI-only condition controlled for exposure to the platform but not for the extra instructional variety and relational attention embedded in the psychology activities, and self-reported affective measures remain more vulnerable to expectancy effects than the blind-rated proficiency scores. The ten-week duration leaves open questions about whether the gains persist, and the sample of twelve universities in general English courses cannot represent the Middle East’s considerable diversity.

Even with those caveats, the study carries a message that is likely to resonate far beyond the three countries where it was conducted. Artificial intelligence in education is usually evaluated on technical sophistication, adaptivity, and efficiency. This research argues that such metrics miss the point: learners experienced the AI system as a relational and emotionally mediated presence, and its effectiveness depended on teacher mediation, cultural responsiveness, and the deliberate cultivation of positive emotion. Language programs, the authors suggest, should move beyond using AI merely for correction and drill, pairing AI tasks with brief reflective or strengths-based activities, and training teachers to interpret AI feedback in emotionally supportive terms. Future research will need active control conditions matched for time-on-task, longer follow-up periods, and possibly AI systems designed from the ground up around affective and motivational principles. But the headline finding is already clear: the smartest educational technology may be the kind that knows when to let human warmth lead.

Subject of Research: Integrating positive psychology principles with AI-supported English as a Foreign Language instruction in higher education

Article Title: A human-centered approach to integrating positive psychology and artificial intelligence in EFL instruction

Article References: Isaee, H., Barjesteh, H., & Rad, N. F. (2026). A human-centered approach to integrating positive psychology and artificial intelligence in EFL instruction. Discover Education, 5(1), Article 955. https://doi.org/10.1007/s44217-026-02151-z

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02151-z

Keywords: artificial intelligence in education, positive psychology, EFL pedagogy, foreign language enjoyment, human-centered AI, technology-enhanced language learning, PERMA model, academic buoyancy, teacher mediation, ChatGPT, language proficiency, learner well-being

The research team argues that most current scholarship on artificial intelligence in the workplace has been captivated by performance metrics—productivity gains, cost reductions, service quality improvements—while treating the human cost as an afterthought. Their paper shifts the lens. By framing employee psychological wellbeing as a pillar of human sustainability, they contend that an organization cannot be truly sustainable if the mental health, motivation, and social connectedness of its workers deteriorate as algorithms take over more of the daily workload. The paradox emerges because the same adoption decisions that streamline operations can strip away precisely the experiences that make work psychologically nourishing.

To explain how this erosion happens, the authors build their framework on two well-established pillars of organizational psychology: self-determination theory and recovery theory. Self-determination theory holds that human flourishing at work depends on the satisfaction of three basic psychological needs—autonomy, competence, and relatedness. Autonomy is the sense of volition and control over one’s actions; competence is the feeling of effectiveness and mastery; relatedness is the experience of meaningful connection with others. Recovery theory, meanwhile, explains how workers replenish mental and emotional resources during and after demanding periods, and how chronic demands without adequate recovery produce strain, exhaustion, and psychological distress.

Applied to AI adoption, the theoretical machinery generates a sobering prediction. When intelligent systems automate decisions, standardize workflows, and monitor performance, employees may find their discretion shrinking—their sense of autonomy diminished as algorithmic recommendations dictate the pace and content of their work. Competence can suffer when the skills workers spent years honing are rendered obsolete or when success depends on opaque machine outputs they cannot fully understand. Relatedness may weaken as human interactions are replaced by automated transactions and as workers compete with, rather than collaborate alongside, machine agents. The cumulative result, according to the model, is unsatisfied psychological needs, which translate into psychological distress and a decline in what the authors term human sustainability.

Crucially, the framework does not treat this damaging trajectory as inevitable. The researchers identify two factors that shape how harshly AI adoption bears down on workers: employee AI literacy and the prevailing culture of AI within the organization. AI literacy—the knowledge and skills that allow workers to understand, evaluate, and work effectively with intelligent systems—can either amplify or buffer the paradox. Workers with low literacy may feel threatened, confused, and helpless in the face of AI, accelerating the erosion of competence and autonomy. But the relationship is not straightforward, and the authors emphasize that even high literacy alone does not guarantee positive outcomes if the surrounding organizational culture treats AI purely as a tool for surveillance and cost cutting.

This is where human-centered AI practices enter the model as moderators—variables that can soften or even neutralize the negative pathways. The study highlights four in particular. Transparency means that workers understand what the AI does, how it reaches recommendations, and what data it uses, reducing the anxiety born of opacity. Employee involvement means that staff participate in designing, selecting, and configuring AI systems, which preserves a sense of agency and ownership. AI literacy training equips workers with the competence to collaborate with intelligent tools rather than feel dominated by them. Human oversight ensures that meaningful decisions remain with people, keeping algorithmic systems in an advisory role rather than an authoritarian one.

When these practices are in place, the researchers suggest, the corrosive link between AI adoption and psychological need satisfaction can be tempered. An organization that deploys AI transparently, involves its employees, invests in their understanding, and retains human judgment at critical junctures can capture efficiency gains without paying the psychological price. Conversely, an organization that rolls out intelligent systems abruptly, opaquely, and without worker voice may find that short-term productivity is purchased with long-term burnout, disengagement, and turnover—a trade-off that ultimately defeats the goal of sustainability.

The practical implications the authors draw for managers are concrete. They call for participatory AI design processes in which employees help shape the systems that will share their workplaces. They urge organizations to preserve employee discretion, deliberately carving out spaces where human judgment remains authoritative. They recommend systematic assessment of employees’ psychological health and wellbeing as part of AI adoption programs, treating mental health indicators with the same seriousness as operational performance dashboards. And they argue that AI adoption strategies should be synchronized with the United Nations Sustainable Development Goals—specifically SDG 3 on good health and wellbeing, SDG 8 on decent work and economic growth, and SDG 9 on industry, innovation, and infrastructure—so that technological transformation aligns with, rather than contradicts, broader sustainability commitments.

The study also situates itself within the fast-growing literature on responsible AI, but with a distinctive reorientation. Whereas much of that literature concentrates on fairness, bias, privacy, and accountability of algorithms, this paper insists that the human experience of working alongside AI deserves equal analytical weight. Moving from an efficiency-focused lens to a human sustainability lens, the authors contend, is not merely an ethical nicety but a strategic necessity for organizations that intend to thrive over decades rather than quarters. Their conceptual model is explicitly designed as a foundation for empirical testing, and the researchers invite scholars to validate the framework across different industries, organizational types, and cultural settings—particularly relevant given the international composition of the team, which spans Gulf, Middle Eastern, and South Asian research institutions.

As artificial intelligence accelerates into every corner of the service economy, the message of this research lands with urgency. The paradox it identifies—efficiency gains that quietly hollow out the human core of work—will not resolve itself. Organizations that treat AI adoption as a purely technical project risk discovering, too late, that their most valuable asset, a psychologically healthy and motivated workforce, has been degraded in the process. The study’s framework offers a way forward: embed transparency, participation, literacy, and human oversight into the fabric of AI deployment, and measure success not only in output but in the wellbeing of the people doing the work. In an era when the race to automate is fierce, the research suggests that the true finish line is not the smartest machine, but the most sustainable human-technology partnership.

Subject of Research: The sustainability paradox of artificial intelligence adoption and its effects on workers' psychological wellbeing and human sustainability

Article Title: Sustainability paradox of artificial intelligence adoption for workers’ wellbeing

Article References: Al Mazroa, A., Khan, S. H., Dwikat, S. Y., Asif, M., Hanaysha, J. R., & Banalzwaa, H. S. (2026). Sustainability paradox of artificial intelligence adoption for workers’ wellbeing. Discover Sustainability. https://doi.org/10.1007/s43621-026-04671-y

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04671-y

Keywords: artificial intelligence, sustainability paradox, workers wellbeing, self-determination theory, recovery theory, AI literacy, human-centered AI, psychological need satisfaction, human sustainability, responsible AI, employee autonomy, organizational culture