Implement AI in assessment as a hybrid support tool for low-stakes, formative stages while humans retain final grading authority¶

Strategy · All strategies
Evidence · no claims cited

Description¶

The article's implications recommend that institutions "avoid positioning AI as a replacement for human grading and instead adopt hybrid models in which AI supports formative feedback, early drafting, and lower-stakes evaluation, while human instructors retain responsibility for final judgment". AI feedback should be "clear, structured, and easy to use for revision", focused on technical aspects such as grammar and organization, with transparency and human oversight kept in place to ensure trust. AI should be integrated into drafting and improvement stages rather than final grading decisions.

Design Implications¶

Context¶

Requirements¶

• AI systems must be open and understandable, with human oversight maintained; implementation must complement, not displace, meaningful instructor feedback.

Constraints¶

• Students remain skeptical of AI for creative, subjective, or high-stakes evaluation, so the strategy is limited to low-stakes and formative contexts.

Target Learners¶

• undergraduate students in higher education

Target Learning Goals¶

• effective use of AI-generated feedback to support revision and writing improvement

Affordances¶

• Perceptions Preferences Distinction Ai Assessment

Related Strategies¶

• Deploy AI-assisted classroom observation as a complementary first-pass screening and reflection tool, with raters retaining interpretive judgment
• Use automatic reflection classification for preliminary feedback and workload support, with final assessment retained by educators
• Allocate human and AI effort by dimension type: automate explicit-criteria dimensions, retain human review for pedagogical judgment
• Use AI chatbots to assist human-led coaching with routine support between sessions while humans retain challenge and goal clarification
• Use reward-aligned LLM scoring as a teacher-supervised auxiliary tool for preliminary essay scoring at scale
• Use early cheating-risk predictions only for low-stakes support with human review, never for disciplinary decisions
• Implement AI-assisted report feedback through a four-stage phased pathway beginning with mature low-level tools

Examples¶

-

Key Sources¶

• Yildirim-Erbasli, S. N., Ilgun Dibek, M., Thomas, M. L., & Lesoway, N. (2026). When Do Undergraduate Students Prefer AI? Insights into AI Scoring and Feedback. Behavioral Sciences, 16(7), 1196. https://doi.org/10.3390/bs16071196