Agentic education · Learning science

Actionable companion · Motivation & engagement

From AI evidence to responsible practice: ChatGPT-Assisted Learning Effectiveness and Students’ Academic Outcomes: A Mediation Analysis of Cognitive Engagement and Self-Efficacy

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not findings or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

Read original paperSearch paper agentCC BY 4.0

Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Higher education. Verify the participants, intervention and comparator before applying this study in a new setting.

Original authors’ abstract, reproduced under the linked source license:

Abstract Grounded in Social Cognitive Theory, this study examines the association between ChatGPT-assisted learning effectiveness and students’ academic performance, emphasizing the mediating roles of cognitive engagement and academic self-efficacy. Data were collected from tourism and hospitality students at Egyptian universities. Findings indicate that perceived ChatGPT-assisted learning effectiveness is positively associated with students’ academic performance, cognitive engagement, and academic self-efficacy. Cognitive engagement and academic self-efficacy also show significant positive associations with academic performance, and both constructs significantly mediate the relationship between ChatGPT-assisted learning effectiveness and academic performance. These findings suggest that the educational value students attribute to ChatGPT may operate through complementary cognitive and motivational pathways, while the cross-sectional design prevents causal conclusions. Keywords: ChatGPT-assisted learning effectiveness, academic performance, cognitive engagement, academic self-efficacy, tourism and hospitality education, higher education

What kind of evidence is this?

Supporting AI learning science · Survey / observational study

Stage: Higher education. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

This study investigates the relationship between ChatGPT-assisted learning effectiveness (CALE) and academic performance (AP) among hospitality and tourism students, with particular attention to the mediating roles of cognitive engagement (CE) and academic self-efficacy (ASE). The conceptual model was established based on a comprehensive examination of relevant theoretical and empirical research, which provided the basis for developing the proposed hypotheses and specifying the relationships among the study constructs. To empirically examine the proposed model, primary data were obtained through a structured questionnaire administered to hospitality and tourism students enrolled at public universities in Egypt. The collected data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM), which enabled simultaneous evaluation of the measurement model and structural relationships, including the proposed indirect relationships. In addition, procedural and statistical approaches were incorporated to assess the possibility of common method bias (CMB).

Limitations and uncertainty · source passage

Despite its theoretical and empirical contributions, this study has certain limitations that should be acknowledged and addressed in future research. Recognizing these limitations provides opportunities to further refine understanding of ChatGPT-assisted learning and its role in higher education.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

  • Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Choose one disciplinary task. Require a first attempt, a documented AI consultation, a critique of the output and an independent explanation. Align the comparison group and scoring rubric with the same objective. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Blind-score reasoning and accuracy on a new task without AI. Distinguish model examination scores, student perceptions and actual learner gains; only the last directly tests educational benefit. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Ahmed Mohamed Hasanein; Bassam Samir Al-Romeedy; Hazem Ahmed Khairy; Nadir Aliane. ChatGPT-Assisted Learning Effectiveness and Students’ Academic Outcomes: A Mediation Analysis of Cognitive Engagement and Self-Efficacy. European Journal of Investigation in Health, Psychology and Education (2026-09-27). https://doi.org/10.3390/ejihpe16100150

Companion prepared 2026-11-01. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.