The timing of the study is significant. Tunisia launched its national e-learning Mentor initiative, known as ELM, as a major government investment in AI-based educational resources for the 2024 to 2025 academic year. The researchers, Ichrak Riahi and Wafa Battikh of the University of Jendouba and Hanen Khanchel of the University of Carthage, collected their survey data between March and May 2024, capturing student perceptions at a pivotal moment just before full implementation. This gave them a snapshot of expectations and attitudes at the exact point when the country’s universities were committing to a sweeping digital transformation.

The theoretical backbone of the study is the Technology Acceptance Model, or TAM, first proposed by Fred Davis in 1989 and still one of the most widely used frameworks for predicting how people adopt new technologies. TAM centers on two constructs: perceived usefulness, meaning the degree to which a user believes a technology will improve their performance, and perceived ease of use, meaning how effortless the technology feels to operate. The Tunisian team extended this classic model by adding electronic trust, satisfaction, and student engagement as downstream outcomes, and then introduced a variable rarely tested in this context: the ethical perceptions students hold about smart learning tools.

The statistical results reveal a clear hierarchy of influence. Perceived usefulness significantly boosted electronic trust, with a path coefficient of 0.122, while perceived ease of use had an even stronger effect at 0.257. In plain terms, students who found smart technologies helpful and simple to navigate were markedly more inclined to trust them. Ease of use appears to work by making the learning experience feel manageable and intuitive, reducing the sense of risk that often accompanies unfamiliar digital systems. This finding aligns with a broad international literature showing that usability is a gateway to confidence in online environments.

Trust, in turn, proved to be the single most powerful driver of engagement in the entire model. The path from electronic trust to student engagement registered a coefficient of 0.352, and trust also strongly predicted satisfaction at 0.416. Satisfaction itself fed back into engagement with a coefficient of 0.383, suggesting a virtuous cycle: students who trust a platform become satisfied with it, and satisfied students invest more of themselves in the learning it delivers. The researchers describe satisfaction as playing a complementary mediating role, channeling some of trust’s influence on engagement while trust also exerts a direct effect of its own.

The most novel contribution of the study lies in its treatment of ethics. The researchers hypothesized that students’ ethical perceptions, covering concerns such as data privacy, algorithmic bias, and the responsible use of artificial intelligence, would moderate several of these relationships. The results were nuanced. Ethical perception did not significantly moderate the link between trust and engagement, with a p-value of 0.111 falling short of the 0.05 threshold. However, it did significantly strengthen the effect of trust on satisfaction, with an unstandardized coefficient of 0.0918 and a p-value of 0.045, and it magnified the effect of satisfaction on engagement, with a coefficient of 0.1091 and a p-value of 0.021.

These moderation findings carry a striking implication: ethics is not a peripheral concern but an amplifier of the emotional and motivational pathways that make students stick with smart learning technology. When students believe a platform respects their data and operates fairly, their trust converts more readily into satisfaction, and their satisfaction converts more readily into deep engagement. The researchers draw on fairness heuristic theory to explain this pattern, noting that people gravitate toward entities they perceive as ethical and that ethical behavior signals respect for user rights and values. When a technology’s conduct aligns with a student’s moral identity, attitudes toward that technology warm considerably.

The study is candid about its limits. The cross-sectional, self-report design cannot establish causality, and the convenience sample, while deliberately diverse across disciplines and degree levels, was drawn from a single country during a single semester. The moderating effects, though statistically significant, explained only modest amounts of additional variance, and the overall model accounted for a small portion of engagement overall, hinting that other unmeasured factors, from instructor quality to home internet access, also shape how students engage with smart tools. The authors call for longitudinal designs, cross-cultural comparisons, and qualitative work using interviews and focus groups to probe how students actually reason about the ethics of educational AI.

For policymakers and university leaders, the practical recommendations are concrete. The authors urge the creation of national ethical standards for educational AI, addressing algorithmic transparency, data privacy, and responsible use, developed by working groups that include educators, technologists, and ethicists. They recommend funding digital literacy and ethics programs embedded in curricula and professional development, and they stress equity of access, arguing that digital inequalities across Tunisian universities must be addressed so that students with disabilities and those at under-resourced institutions are not left behind. Educators, meanwhile, are encouraged to position AI as a support for teaching rather than a substitute for it, keeping human feedback loops at the center of the learning experience.

What makes this research resonate far beyond Tunisia is its reminder that the adoption curve of educational technology is not purely a matter of features and functionality. As universities worldwide pour resources into AI tutors, virtual reality laboratories, and adaptive learning platforms, the Tunisian findings suggest that the return on those investments will hinge on trust built through usability, satisfaction earned through genuine usefulness, and, crucially, ethical credibility that students can perceive and verify. In an era when students increasingly weigh the moral dimensions of the AI tools they use, institutions that treat ethics as a design requirement rather than an afterthought may find their smart learning technologies not just adopted, but embraced.

Subject of Research: The impact of smart learning technologies on student engagement, e-trust, and satisfaction in higher education, with ethical perceptions as a moderator

Article Title: Exploring the impact of smart learning technologies on student engagement, e-trust, and satisfaction in higher education institutions: the moderating role of ethical perceptions

Article References: Riahi, I., Battikh, W., & Khanchel, H. (2025). Exploring the impact of smart learning technologies on student engagement, e-trust, and satisfaction in higher education institutions: the moderating role of ethical perceptions. Journal of New Approaches in Educational Research, 14(1), Article 21. https://doi.org/10.1007/s44322-025-00039-8

Image Credits: AI Generated

DOI: 10.1007/s44322-025-00039-8

Keywords: smart learning technology, higher education, student engagement, e-trust, technology acceptance model, ethical perception, e-learning, Tunisia, artificial intelligence in education, structural equation modeling, student satisfaction, data privacy