Does AI help students learn or make them lazier?

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1. 1

   Enhancing engagement and self-efficacy through AI-supported Chinese language instruction: a mixed-methods intervention study.

   Frontiers in psychology (Zhai)Published Jul 29, 2026Checked Oct 10, 2026

   “Measures of engagement and self-efficacy were collected before and after the intervention to examine changes within and between groups. Mixed-design ANOVA revealed significant Group × Time interaction effects for both engagement (partial η 2 = 0.165) and self-efficacy (partial η 2 = 0.116), indicating significantly greater improvements in the AI-supported group than in the traditional group. Results showed that students in the AI-supported group experienced significant gains in engagement and self-efficacy, whereas the traditional group showed little change. Following the quantitative phase, semi-structured interviews were conducted to explain the quantitative findings. Qualitative analyses helped explain these findings, indicating that AI-supported activities fostered active engagement, enhanced cognitive processing, promoted positive emotional experiences, and increased confidence in Chinese language knowledge and production. The study highlights the potential of AI tools to enhance motivation and psychological outcomes in language learning and provides practical recommendations for implementing AI in the classroom through effective teacher support and task design.”

2. 2

   Artificial Intelligence in Dental Education: Overview of Teaching, Assessment and Academic Performance Prediction.

   International dental journal (Chuenjitwongsa et al.)Published Jul 11, 2026Checked Oct 10, 2026

   “Forty-two publications met the inclusion criteria and were used as the evidential basis for this study. AI integration emphasised fundamental knowledge, use cases, and evaluation in teaching and learning. AI tools such as chatbots, simulations, and generative models enhanced student engagement and learning efficiency while supporting clinical decision-making. AI-assisted tasks, including radiograph interpretation and scientific writing, demonstrated improved outcomes. For assessment, AI showed moderate to high correlations with human evaluators for essay grading and thematic analysis. Further, AI models could forecast student outcomes, supporting personalised learning strategies and early intervention. AI applications have a vast potential to enhance dental education, particularly in improving teaching efficiency, personalised learning, self-directed learning, and assessment accuracy. Curriculum revisions and faculty training are necessary to fully integrate AI responsibly, leveraging its capabilities while maintaining the critical role of human oversight in education.”

3. 3

   Digital personas of AI use in nursing education: a latent class analysis of learning behaviors and academic engagement.

   Frontiers in medicine (Aljabri et al.)Published Aug 14, 2026Checked Oct 10, 2026

   “The profiles included strategic engagers, moderate users, and passive or low engagers. As expected, strategic engagers were characterized by high levels of engagement with all kinds of behaviors studied, whereas Passive used consistently reported low levels of engagement. Usage indicators of AI revealed little variation among classes. The performance scores of strategic engagers were significantly better compared with those of passive users (β = 2.39, 95% CI = 2.08-2.69). No significant association was found for demographic characteristics.ConclusionsNursing students can be categorized into distinct profiles based on their patterns of AI use and study engagement. Our findings showed that study engagement, but not AI use alone, was the key factor associated with academic performance. These findings highlight the importance of promoting effective learning strategies alongside AI integration. Educational interventions should focus on guiding students toward strategic and responsible use of AI to enhance learning outcomes.”

4. 4

   Beyond checking: verification quality, reliance calibration, and learning in generative AI-assisted higher education.

   Frontiers in psychology (Wei & Shang)Published Sep 11, 2026Checked Oct 10, 2026

   “Reliance calibration is treated separately as an output-contingent classification of reliance decisions. Targeted Web of Science Core Collection searches (2022-2026) yielded 493 unique records; 10 related reviews and 14 priority empirical studies were examined. The searches were targeted rather than systematic. Among the 14 priority studies, none jointly measured verification success and subsequent reliance calibration against an independently adjudicated reference standard; delayed retention or transfer was also uncommon. We identify boundary conditions including prior knowledge, learner characteristics, task stakes, task verifiability, verification costs, accountability, AI system/configuration, and multidimensional AI literacy. The map is an analytic ordering rather than a validated causal model. Educational interventions should therefore be evaluated for the specific process they target, from verification quality to reliance decisions and learning that persists without AI support.”

5. 5

   Promise, Mimicry, and Surveillance: Responsibly Integrating Artificial Intelligence With Socratic Inquiry in Medical Education.

   JMIR medical education (Sánchez)Published Sep 10, 2026Checked Oct 10, 2026

   “The second is what I term the Panopticon Paradox: the data collection that makes AI tutoring effective may erode the psychological safety that Socratic inquiry requires, pushing learners toward performative rather than authentic engagement, in a manner analogous to gaming behaviors documented in intelligent tutoring systems. I present this second construct as a testable causal model with specified mediators, moderators, and falsifiable predictions rather than as an established finding. Because the evidence base is dominated by proof-of-concept tools, cross-sectional surveys, and short-term evaluations, I argue that AI is complementary rather than a replacement technology, and I propose a 3-pillar framework (governance, curriculum, and faculty development) tied explicitly to the 2 failure modes. I distinguish 4 modalities of delegation and argue that ethical deliberation, emotionally complex communication, and ambiguous clinical judgment cannot presently be recommended for autonomous or primary AI delivery.”

6. 6

   Artificial intelligence in education (Wikipedia)

   WikipediaPublished Oct 10, 2026Checked Oct 10, 2026

   “Artificial intelligence in education (often abbreviated as AIEd) is a subfield of educational technology that studies how to use artificial intelligence (AI) to create learning environments. Considerations in the field include data-driven decision-making, AI ethics, data privacy, and AI literacy. Concerns include the potential for cheating, over-reliance, equity of access, reduced critical thinking, and the perpetuation of misinformation and bias.”