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Using Generative AI to Promote Psychological, Feedback, and Artificial Intelligence Literacies in Undergraduate Psychology

● high Β· 2026-08-15 Β· generative-ai, ai-literacy, feedback, ai-feedback-quality, assessment, critical-thinking, academic-integrity, higher-ed, writing-education

Summary

Richmond & Nicholls (2025) describe integrating generative AI (ChatGPT) into a large second-year psychology assessment β€” the "Media Assignment," in which students translate a published research article into a media release and video for a lay audience. They modified Phase 1 so that instead of peer review, students used ChatGPT to generate a draft media release and then critiqued that output against the course's marking rubric, grading six criteria and identifying three strengths, three weaknesses, and three priorities for revision. In Phase 2 students revised the AI text (tracked changes), and in Phase 3 they produced a final video. Students accurately assessed the ChatGPT output: they rated it stylistically strong but lacking accurate coverage of the research's aims, methods, and results. Working with the rubric and genAI early had a small but significant benefit for script-revision grades relative to the prior peer-review cohort, though final video grades did not differ. The design is presented as building three literacies at once β€” psychological, feedback, and AI β€” and as an example of shifting Assessment emphasis from product to learning process.

Key Findings

  • Rubric-based critique of genAI output: Students graded a ChatGPT-drafted media release against the marking rubric; 92% rated the output at pass or above on "Style," but 94% rated it at credit level or lower on "Aims/Methods/Results" β€” recognizing strong style alongside inaccurate coverage of aims, methods, and results (χ²(4) = 317.48, p < .001; Cramer's V = 0.66).
  • Early rubric engagement helped later performance: The 2023 AI-critique cohort scored higher than the 2022 peer-review cohort on the script revision task (t(733.05) = 4.93, p < .001, d = 0.36); no significant difference on the final video (t(732.48) = 1.52, p = .129). The authors caution the gain could stem from starting from stylistically strong ChatGPT output rather than rubric engagement.
  • Scaffolding gap: A small number of students failed to catch coverage inaccuracies (awarding ChatGPT High Distinction/Distinction on "Aims/Methods/Results"), indicating some need more Scaffolding of the Critical Thinking process for evaluating genAI veracity.
  • Three literacies at once: Psychological literacy (translating research for a lay audience), feedback literacy (early rubric engagement, candid critique without peer-reaction concerns, self-evaluation), and AI literacy (understanding, working with, and critically evaluating AI) are promoted by one integrated assessment.
  • Process over artifact: The authors argue genAI's ability to mimic human artifacts pushes educators to assess the learning process, not the final product, and to use AI to outsource initial creation so students practice higher-order evaluation and analysis.

Implications

  • Offers a replicable template for using genAI in Assessment to train evaluation and analysis rather than banning the tool.
  • Uses ChatGPT output as a "teachable artifact" β€” stylistically good but factually unreliable β€” giving students practice detecting hallucination and appraising evidence.
  • Shows feedback literacy can be built by having students grade AI output against a rubric early in a course, with observable grade benefits on later revision tasks.
  • Supports Assessment reform toward documented process (revision, version control) over deliverable artifacts in the age of generative AI.

Connected Concepts

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Citation

Richmond, J. L., & Nicholls, K. (2025). Using generative AI to promote psychological, feedback, and artificial intelligence literacies in undergraduate psychology. Teaching of Psychology, 52(3), 291–297. https://doi.org/10.1177/00986283241287203