Evaluate LLM-supported learning beyond binary access by measuring enacted interaction and preserving no-help assessments¶

Strategy · All strategies
Evidence · 1 claim (1 mixed) · 1 study (1 associational), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description¶

The article recommends that AIED evaluation move beyond binary LLM access by measuring enacted learner–LLM interaction quality and preserving no-help assessments, because "AI -assisted practice scores may reflect real -time support rather than retained competence." Candidate scaffolds named for future evaluation include prompt starters, hint-first workflows, reflection checkpoints, and verification cues. Independent quizzes, written explanations, oral checks, or transfer tasks are proposed as complementary evidence of learning.

Design Implications¶

Context¶

Requirements¶

• No-help assessments or alternative independent evidence such as written explanations, oral checks, or transfer tasks to complement AI-assisted practice scores

Constraints¶

• The authors state these are design hypotheses from a small single-course study rather than general prescriptions

Target Learners¶

• undergraduate students in courses integrating LLM access

Target Learning Goals¶

• independently demonstrated conceptual learning and retention rather than AI-assisted task completion

Claims¶

• Students' stated pre-interaction intentions only weakly correspond to their enacted help-seeking with an LLM [~W]

Related Strategies¶

• Evaluate AI literacy interventions with behavioral measures of LLM interaction in addition to self-reports
• Configure the level of AI assistance per assignment according to learning objectives
• Move beyond static skill-targeted rewriting toward interaction-aware adaptive error explanations
• Develop AI literacy frameworks that build metacognitive awareness distinguishing system-assisted performance from independent competence
• Complement outcome measures with continuous process measures to assess social effects and enable adaptive pedagogical agents

Examples¶

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Key Sources¶

• Mohammad Amanlou, Yasaman Amou-Jafari, Fereshte Bagheri, Fatemeh Boloukazari, Mehrad Liviyan, Elahe Khodaverdi Nadrabadi, Shahab Sherafat, Behnam Bahrak. (2026). Beyond Access: Guided LLM Scaffolding for Independent Learning in Undergraduate Statistics. Proceedings of the 34th International Conference on Computers in Education. https://github.com/MohammadAmanlou/Guided-LLM-Scaffolding