Making sense of AI feedback: how students' feedback literacy moderates the link between ChatGPT acceptance and self-regulated learning

Synthesis: Feedback literacy — specifically, students' ability to make sense of AI-generated feedback — is the boundary condition that determines whether ChatGPT acceptance translates into perceived self-regulated learning benefits.

Core Finding

In a survey of 211 Hong Kong secondary students (Grades 7–9), Mendoza, Xiong and Yan found that all five Technology Acceptance Model (TAM) components (perceived usefulness, perceived ease of use, attitude, intention to use, actual use) positively predicted self-reflection. But every one of these links was moderated by feedback sense-making: students with stronger feedback-processing skills reported greater self-regulatory benefits from ChatGPT use, while those with weaker skills showed minimal or even negative associations.

Design & Measures

• Sample: 211 secondary students (Grades 7–9, ages 12–15; 51% female; 102 G7, 49 G8, 60 G9) from a government-funded Hong Kong school running AI literacy initiatives. Data collected October 2023 via online survey; nine of 220 initial respondents excluded for suspicious patterns/outliers.
• Acceptance: 25-item ChatGPT Technology Acceptance instrument (adapted from Davis, 1989; reliabilities .90–.95), measuring Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude (ATT), Intention to Use (ITU), and Actual Use (AU).
• Moderator: 4-item Making Sense of Feedback subscale from Dawson et al.'s (2024) Feedback Literacy Behavior Scale, adapted to reference ChatGPT (α=.94) — capturing credibility assessment, decision to use, judgment of conflicting comments, and standards alignment.
• Outcome: 7-item self-reflection subscale from Yan's (2018) Self-assessment Practice Scale (α=.93).
• Analysis: Hierarchical multiple regression (demographics controls → main effects → interaction), Johnson-Neyman technique, and simple-slope analyses at low/moderate/high sense-making. >90% power for medium effects; VIFs <3.5.

Results

Main effects (H1 — supported)

All five TAM components significantly predicted self-reflection after controlling for age, gender, and socioeconomic status:

Moderation (H2 — supported)

Feedback sense-making significantly moderated each link (interactions β=.11–.20, all p<.05; full models R²=.28–.34; ΔR² from step 2 = .124–.234).

• PU & PEOU: significant associations with self-reflection emerged only at moderate-to-high sense-making (Johnson-Neyman thresholds 3.89 and 3.55). Slopes were non-significant at low sense-making.
• Attitude: the positive relationship appeared only at high sense-making (above 4.26).
• Intention to use: a crossover interaction — negative at low sense-making (β=-.17, p=.01) but positive at high (β=.15, p=.02). Students with weak evaluation skills but strong intentions to use ChatGPT may be exhibiting over-reliance rather than strategic use.
• Actual use: the interaction was significant but only for extreme-low sense-making (below 2.00, outside the observed range); within typical student capabilities there was no reliable conditional relationship.

Methodological Strengths & Caveats

• Strengths: strong reliability across all scales, power analysis for regression, assumption checks, and Johnson-Neyman analyses that map where effects turn significant.
• Caveats: cross-sectional design (no causal inference), self-report measures rather than observed behaviors or objective outcomes, single educational system, and data collected during the early-positive-coverage adoption period of ChatGPT.

What this means for practice

• Instructors. Teach students to make sense of AI feedback — judging its credibility, deciding whether to use it, reconciling conflicting comments, and aligning it with standards — before expecting ChatGPT acceptance to translate into self-regulation.
• Instructors. Read strong intention to use with weak evaluation skills as a warning sign: that combination predicted lower self-reflection, a pattern the authors interpret as over-reliance rather than strategic use.
• Administrators. Tier support by feedback-literacy level — universal instruction in evaluating AI credibility, scaffolded interaction for moderate-literacy students, and independent use only for high-literacy students.
• Administrators. Use the Feedback Sense-Making subscale to screen, applying the authors' 4.2 threshold to identify which students report benefiting from ChatGPT use and should not be left to use it unsupervised.

Limitations

• Cross-sectional survey of 211 secondary students (Grades 7–9, ages 12–15) in one government-funded Hong Kong school, so the moderation results cannot show that feedback literacy causes the self-regulation benefits.
• Every construct was self-reported — acceptance, feedback sense-making, and self-reflection — with no observed revision behavior and no objective learning outcome.
• Data were collected in October 2023, during the early, largely positive-coverage adoption period for ChatGPT, which may have inflated the acceptance associations the study reports.

Citation

Mendoza, N. B., Xiong, Y., & Yan, Z. (2026). Making sense of AI feedback: how students' feedback literacy moderates the link between ChatGPT acceptance and self-regulated learning. Educational Psychology. DOI: 10.1080/01443410.2026.2641521. CC BY.