Peer Pressure and Quality: What Drives Bangladeshi Students to Embrace ChatGPT

Artificial intelligence has swept through university lecture halls across the globe, and few tools have reshaped student life as rapidly as ChatGPT. Yet the question of why some students embrace conversational AI while others hesitate has remained stubbornly difficult to answer, particularly in developing economies where digital infrastructure and academic support systems vary widely. A new study published in Discover Education offers one of the most detailed portraits to date of ChatGPT adoption in Bangladesh, a country racing to digitize its higher education sector. Drawing on survey responses from 640 university students, researchers found that the decision to adopt ChatGPT is not simply a private calculation of costs and benefits. Instead, it is a socially constructed process, filtered through the opinions of peers, teachers, and online communities before it ever becomes a habit.

The research team, led by Kamron Naher of Presidency University in Dhaka alongside colleagues from institutions across Bangladesh and China, built their investigation on a fusion of two classic frameworks in information systems research. The first is the Technology Acceptance Model, or TAM, which holds that two beliefs govern whether people adopt a technology: perceived usefulness, the sense that a tool improves performance, and perceived ease of use, the sense that it requires little effort. The second is the DeLone and McLean IS Success Model, which evaluates systems through quality dimensions such as the calibre of information delivered, the responsiveness of interactions, and the technical reliability of the platform itself. By stitching these frameworks together and adding social influence from the Unified Theory of Acceptance and Use of Technology, the researchers created a model capable of testing how system-level qualities and social pressures jointly shape student attitudes.

The data collection ran from January to April 2025 using a structured online questionnaire distributed through Facebook, Messenger, WhatsApp, and email networks, with QR codes shared across academic communities. Respondents were screened to confirm prior experience with ChatGPT, ensuring that every participant could evaluate the tool from direct use rather than hearsay. Of 657 initial responses, 640 survived rigorous screening for completeness and consistency. The sample skewed male at 55.63 percent, with nearly half of participants aged between 21 and 30, and just over half enrolled at public universities. Business students formed the largest disciplinary group at 25 percent, followed by respondents from engineering and information technology fields.

Analysis proceeded through partial least squares structural equation modeling, a statistical technique well suited to testing networks of relationships among latent psychological constructs. The researchers first verified their measurement model, confirming internal consistency with Cronbach’s alpha and composite reliability values above accepted thresholds, and establishing convergent and discriminant validity through average variance extracted, the Fornell-Larcker criterion, and heterotrait-monotrait ratios. Common method bias was ruled out using the full collinearity approach, with all variance inflation factors falling below the 3.3 cutoff. The structural model was then tested with 10,000 bootstrap subsamples, and the resulting coefficient of determination reached 0.91 for the endogenous variables, indicating that the integrated framework explains an exceptionally large share of the variance in the outcomes it models.

Every one of the eleven hypotheses was supported. Information quality, meaning the accuracy, relevance, timeliness, and clarity of ChatGPT’s outputs, emerged as a powerful driver of perceived usefulness. This finding carries particular weight in Bangladesh, where formal academic support can be scarce and students often depend on self-directed learning. When the chatbot returns answers that are correct, well-structured, and pertinent to coursework, students come to see it as a genuine performance enhancer rather than a novelty. Interaction quality, defined as the responsiveness, coherence, and conversational richness of the exchange, significantly boosted perceived ease of use, confirming that natural, context-aware dialogue lowers the friction of learning to work with generative AI.

System quality, encompassing reaction speed, dependability, and simplicity of the platform, influenced both perceived usefulness and perceived ease of use, echoing earlier findings from e-learning research. Perceived usefulness and perceived ease of use were mutually reinforcing, and both fed directly into students’ attitudes toward ChatGPT. Attitude, in turn, proved to be the single most consequential predictor of actual adoption, with an importance-performance map analysis quantifying its total effect on adoption at 0.47. The researchers also applied Necessary Condition Analysis, a complementary method that identifies whether predictors are prerequisites rather than mere contributors. Effect sizes ranged from 0.22 to 0.40, with social influence showing a medium effect and the remaining constructs showing large effects, and all permutation tests reached significance at the 0.05 level.

The headline discovery, however, concerns the moderating role of social influence. In Bangladesh’s collectivistic academic culture, the positive relationship between attitude and adoption was significantly strengthened by the encouragement of peers, the endorsement of early users, and supportive institutional environments. Students did not simply assess ChatGPT’s quality in isolation and decide for themselves; they calibrated their judgments against the visible enthusiasm of friends, classmates, faculty, and online communities. The authors argue that this finding challenges Western, individualistic models of technology acceptance, in which adoption is typically portrayed as a solitary cognitive decision. In a developing South Asian educational ecosystem, systemic quality metrics do not automatically translate into use. They are heavily filtered through social structures before becoming behaviour.

The theoretical contribution lies in demonstrating that TAM, the DeLone and McLean model, and UTAUT are not redundant alternatives but complementary lenses whose integration is conceptually necessary. The study also refines the meaning of interaction quality for generative AI specifically, showing that prompt-dependent, conversational systems expand the traditional boundaries of technology acceptance theory in higher education. Earlier work by Almulla in 2024 had established that quality parameters matter for smart learning systems, but the Bangladeshi study goes further by modeling adoption as a socially certified phenomenon and by shifting the analytical lens to a developing economy where peer momentum acts as a catalyst amplifying every direct pathway in the model.

The practical implications are equally pointed. The authors urge university administrators to abandon top-down mandates in favor of peer-led learning circles and collaborative workshops where students collectively model ethical and productive AI use. Institutions are advised to craft clear, context-specific AI guidelines and robust data-handling frameworks to address student anxieties about privacy and academic integrity, transforming conversational AI into a trusted learning assistant rather than a source of institutional risk. Developers, meanwhile, should prioritize onboarding guidance and intuitive interfaces, since early friction discourages novices. The study also acknowledges persistent counterweights: hallucinated information, plagiarism concerns, and data privacy worries remain active background constraints even when social momentum accelerates uptake.

The authors are candid about limitations. The purposive, non-probability sampling design, while necessary given the absence of any centralized database of AI-using students in Bangladesh, restricts generalizability to non-users and other demographics. The cross-sectional design captures attitudes at a single moment and cannot confirm long-term causal trends, and the sample over-represents public universities and business, engineering, and IT students. Roughly 37 percent of the variance in adoption remains unexplained, pointing to factors such as digital literacy, institutional support, prior AI experience, and internet access that future models should incorporate. Longitudinal tracking, cross-cultural comparisons across South Asia, and studies including non-adopters are flagged as priorities. Even so, the message of the research is unmistakable: in the classrooms of emerging economies, the future of AI in learning will be decided not just by the quality of the algorithms, but by the quality of the conversations students have about them with each other.

Subject of Research: Factors influencing ChatGPT adoption among university students in Bangladesh, including the moderating role of social influence

Article Title: Factors affecting ChatGPT adoption among Bangladeshi university students with the moderating role of social influence

Article References: Naher, K., Siddiqi, K. O., Al Mamun, M. A., Chakma, S., Rashid, F., Arju, U. H., Alam, S., & Tanchangya, T. (2026). Factors affecting ChatGPT adoption among Bangladeshi university students with the moderating role of social influence. Discover Education, 5(1), Article 993. https://doi.org/10.1007/s44217-026-02075-8

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02075-8

Keywords: ChatGPT, technology acceptance model, social influence, Bangladesh, higher education, information quality, interaction quality, system quality, PLS-SEM, generative AI, university students, DeLone and McLean model