Embed SRL-oriented AIGC pedagogy: prompt-and-reflect routines, verification rubrics, and feedback literacy¶

Strategy · All strategies
Evidence · 1 claim (1 mixed) · 1 study (1 theoretical), q1 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

The article recommends that instructors scaffold self-regulated learning by designing AIGC activities that explicitly target self-efficacy and motivation rather than seamless answer delivery. It states instructors "can use AIGC to scaffold SRL by embedding prompt-and-reflect routines, feedback literacy activities, and verification rubrics that require students to justify and triangulate AI outputs rather than accept them uncritically." It also suggests intentionally incomplete feedback and verification rubrics may better scaffold SRL than seamless experiences, and that program-level reforms should strengthen enterprise co-development, mentorship, and competency mapping.

Design Implications¶

Context¶

Requirements¶

• Mentors providing autonomy-supportive guidance so positive AI experiences translate into genuine self-efficacy

Constraints¶

• The article cautions that without motivational activation, even high-quality feedback may remain a surface-level benefit; boundary conditions such as discipline, task complexity, and prior AI literacy remain untested

Target Learners¶

• undergraduate students in IEI-linked programs

Target Learning Goals¶

• self-regulated learning
• critical verification of AI outputs
• learning motivation

Affordances¶

• Aigc Affordance Enacted Competence Theory

Claims¶

• No existing AI literacy framework explicitly names prompt construction as a competency [~W]

Related Strategies¶

• Self Regulated Learning Instruction
• Teach AI-mediated judgement and make process, judgement and transfer visible in assessment
• Use the five GAA-LS subscales to target instruction and make AI-related assessment expectations visible in assessment briefs
• Treat AI as a guided support tool and explicitly build verification and skepticism into learning goals
• Make output verification an individually accountable practice within team assignments via rubric requirements
• Deliberately scaffold AI-output verification habits rather than assume they develop alongside rising AI use
• Embed verification routines and cognitive forcing functions into AI-supported learning tasks
• Teachers cultivate approach-oriented goals and help students manage AI-feedback anxiety in AI-assisted language learning
• Embed verification cycles and faded prompt scaffolding into GenAI-supported STEAM tasks
• Embed repeated AI engagement and habit formation into everyday teaching practice, alongside supportive resources and collegial exchange
• Embed media literacy across disciplines from early education and position AI tools as cognitive scaffolds
• Frame GenAI labs explicitly as verification and repair practice alongside prompting

Examples¶

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Key Sources¶

• Liang, Li, Hsu and Xu. (2026). AIGC affordance and student self-regulation in private undergraduate education: a serial mediation model of self-efficacy and learning motivation. Frontiers in Psychology. https://doi.org/10.3389/fpsyg.2026.1800950