How Does Students' Perception of ChatGPT Shape Online Learning Engagement and Performance?

Synthesis: This SEM-based study tested whether students' perception of ChatGPT (knowledge, willingness to use, and concerns) shapes online learning engagement across behavioral, emotional, and cognitive dimensions, and whether engagement in turn drives academic performance. Using survey data from 305 graduate students in Iran, the authors found that ChatGPT perception accounts for 19.2% of variance in engagement, while perception plus engagement together explain 40.4% of variance in academic performance. Online learning engagement significantly mediates the perception→performance link, underscoring its pivotal role in translating AI adoption into academic gains.

Key Findings

Sample: Self-reported survey data from 305 graduate students (master's and doctoral) at Shiraz University, Iran (2024–2025 academic year), drawn from a population of ~7,382 and exceeding the 251 required by power analysis.
Model fit: PLS-SEM showed students' perception of ChatGPT explains 19.2% of the variance in online learning engagement; combining ChatGPT perception and online engagement explains 40.4% of variance in academic performance.
Direct paths: ChatGPT perception had significant direct effects on online learning engagement (β = 0.438) and on academic performance (β = 0.268).
Mediation: Online learning engagement significantly mediated the perception–performance relationship, with a significant indirect effect of β = 0.206 — confirming engagement as a crucial mediator (Hypothesis 4 supported).
Key correlations: Willingness to use generative AI correlated with academic performance (r = 0.412, p < .01); cognitive learning engagement correlated most strongly with performance (r = 0.563, p < .01). Importance–Performance Map Analysis (IPMA) ranked willingness to use ChatGPT as the most important driver of academic performance, followed by cognitive and behavioral engagement.

Study Design & Method

Design: Cross-sectional, self-report questionnaire using a five-point Likert scale (1 = strongly disagree to 5 = strongly agree); stratified sampling across master's and PhD levels for representativeness.
Theoretical grounding: Integrated the Technology Acceptance Model (TAM; Davis, 1989) and Bandura's Social Cognitive Theory (SCT, 1986) to frame how personal, behavioral, and environmental factors shape ChatGPT adoption and engagement.
Perception of ChatGPT (adapted from Chan & Hu, 2023): three subscales — Knowledge of Generative AI Technology (KGAT, 6 items), Willingness to Use Generative AI Technologies (WUGAT, 8 items), and Concerns About Generative AI Technologies (CGAT, 4 items); Cronbach's α = 0.711.
Online learning engagement (adapted from Kuo et al., 2021): 14 items across behavioral (3), emotional (5), and cognitive (6) engagement dimensions; Cronbach's α = 0.921.
Academic performance (adapted from Yu et al., 2010): four items; Cronbach's α = 0.889.
Analysis: Structural Equation Modeling (SEM) via Partial Least Squares (PLS), with Confirmatory Factor Analysis (CFA), Composite Reliability, and Average Variance Extracted (AVE) to validate the measurement model; bootstrapping used for mediation analysis. Reliability (α) ranged 0.711–0.927 and CR 0.661–0.947 across constructs; skewness/kurtosis within acceptable normality thresholds.
Hypotheses: H1 perception→engagement; H2 engagement→performance; H3 perception→performance; H4 engagement mediates perception→performance. All were supported.

Implications for AI in Education

Students' willingness to use ChatGPT is the single strongest lever on academic performance, suggesting that building positive adoption attitudes (perceived usefulness and ease of use, per TAM) may matter more than simply raising awareness.
Because online learning engagement mediates the perception→performance link, AI tools are most effective when they deepen behavioral, emotional, and cognitive engagement rather than merely serving as answer shortcuts — an antidote to the documented risks of overreliance, cognitive offloading, and reduced critical thinking.
Universities should design ChatGPT-based activities that foster interaction and engagement deliberately, while also addressing student concerns (privacy, plagiarism detection, loss of transferable skills) and managing the equity gap in AI access and digital literacy.
Instructors can use the model to predict and intervene: boosting engagement dimensions identified in IPMA (cognitive and behavioral) should translate into measurable academic gains in online settings.

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Citation

Mehrvarz, M., Salimi, G., Abdoli, S., & McLaren, B.M. (2025). How does students' perception of ChatGPT shape online learning engagement and performance?. Computers and Education: Artificial Intelligence, 9, 100459.