Students' self-efficacy for using generative AI increased over the semester, with current self-efficacy significantly higher after the fifth assignment than after the first¶

Claim · All claims
Evidence · 1 study · causal r1 · q2
Currency · newest study 2025 · checked against its sources 2026-10-13 (automated, 1 of 1 entry) · not reviewed by a person

Subclaims¶

q2 i? Current self-efficacy after Assignment 5 was significantly higher than after Assignment 1 (t(177) = 4.25, p = .006). → Daniel M. Oppenheimer 2025

Evidence¶

Daniel M. Oppenheimer 2025¶

Daniel M. Oppenheimer, Trent N. Cash, Allison E. Connell Pensky. (2025). You've Got AI Friend in Me: LLMs as Collaborative Learning Partners. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-025-00521-6

q2 · i? · causal · r1

Survey study using a bespoke three-item self-efficacy scale (αs = 0.75 − 0.86) on 0–100% sliders; n = 81 for Assignment 1 and n = 98 for Assignment 5. Total self-efficacy means rose from 71.7 to 81.2 across the semester (Table 1). No effect size is printed.

"students’ current self-efficacy was significantly higher after the fifth iteration of the assignment than after the first iteration of the assignment ( t(177) = 4.25, p =.006), suggesting growth throughout the semester."

Discussion¶

Learner Variables¶

• Self-Efficacy — outcome: instruction changes it

Related Claims¶

• Proximal goals and ability attributional feedback for early learning raise self-efficacy more than distant goals and effort feedback, by a review's report of earlier studies — the general claim this finding bears on, with the studies behind it
• Students rated the LLM-based assignments as useful, challenging, engaging, and enjoyable at both the first and fifth iterations, with no significant change across the semester — related
• Midterm self-efficacy (comfort and confidence) did not differ between conditions after three assignments, though both varied significantly across algorithmic topics — related
• Students shifted AI usage toward debugging and concept explanation over the semester, with 31 of 89 reporting more debugging use — related
• Students in a scaffolded LLM-collaboration course improved significantly on initial argument quality, prompt engineering, and response to AI feedback from the first to the fifth assignment — related
• LLM usage varied across assignments and tasks, increasing for later assignments (especially discussion points), while Low-Reliance students concentrated use early and reduced it after Midterm 2 — related
• Students’ prompt self-efficacy increased significantly after the scaffolded GenAI course (t = 3.729, p < .001) — related