Beyond Access: Guided LLM Scaffolding for Independent Learning in Undergraduate Statistics

Synthesis: Experimental study comparing Guided vs. Unrestricted Large Language Models (LLMs) access. Explicit training in reasoning-focused scaffolding (stepwise hints, verification) led to significantly better independent performance and Self-Assessment calibration compared to uncritical reliance. This work emphasizes that AI Literacy is a developmental capacity requiring structured Scaffolding and Prompt Engineering discipline. It connects to the need for Curriculum Design that targets Metacognition and Agentic AI rather than just syntax mastery.

Key Findings

• In a four-week quasi-experimental summer program in an undergraduate Probability and Statistics course, students were organized into three balanced conditions: no LLM access, unrestricted LLM access, and guided LLM access.
• The guided condition used the same LLM platform as the unrestricted condition, but students received explicit training and rules intended to promote reasoning-focused Help-Seeking, stepwise hints, verification, and ethical use.
• Guided use was associated with a clearer learning-oriented interaction pattern than unrestricted access, especially in prioritizing reasoning over final answers and requesting stepwise support.
• Guided-LLM students showed a promising pattern of stronger no-help quiz performance during the intervention phase, while unrestricted access appeared more useful for assisted practice completion than for consistently improving independent performance.
• All quizzes and the delayed final exam were completed without LLM or external assistance, separating AI-supported practice performance from independent learning; available time measures did not support a simple duration-based explanation, and self-assessment calibration suggested better alignment between perceived and demonstrated understanding in the guided condition.

Study Design & Method

The design distinguishes between assigned LLM access and the quality of students' actual interaction with the model. The three balanced conditions isolate the effect of guidance: because the guided and unrestricted groups used the same platform, differences can be attributed to training and usage rules rather than tool availability. The use of LLM-free quizzes and a delayed final exam provides a no-help measure of whether AI-supported practice transferred to independent performance.

What this means for practice

• Instructors. Scaffold how students use the model rather than simply granting access: the guided and unrestricted groups shared the same platform, and the guided group showed higher rule-following compliance (2.43/6 vs. 3.21/6, d = 0.81, p = .025) driven mainly by process-oriented Help-Seeking and stepwise hints, while conceptual explanation was common in both groups.
• Instructors. Keep high-stakes assessment LLM-free so assisted practice is not mistaken for learning: every quiz and the delayed final exam in this design was completed without LLM or external assistance, and unrestricted access supported practice-task completion more than independent no-help performance.
• Learners. Preserve the reasoning work by asking for stepwise hints rather than final answers and by working in more turns: guided sessions contained more user turns on average (17.21 vs. 10.54) and more user text (2,645 vs. 1,410 characters).
• Designers. Build prompt starters, hint-first workflows, Reflection checkpoints, and verification checklists into the platform, since Active Learning and verification behaviors were only partially adopted even after training — higher-agency strategies require stronger Scaffolding.
• Instructors. Teach calibrated trust explicitly — when to rely on, question, or verify a model explanation — because the guided group had the highest well-calibrated rate (43%) and the strongest self-assessment–performance correlations, an AI literacy gain rather than mere confidence.

Limitations

• The final analytic sample was 37 of the 57 students who began the program (No-LLM 13, Unrestricted-LLM 12, Guided-LLM 12), after excluding students who missed two or more topic cycles, limiting statistical power.
• Groups were balanced after Week 1 on prior Practice and Quiz scores rather than randomized, so the outcome findings are promising associations rather than definitive causal estimates, the final-exam comparison was directional rather than statistically confirmatory, and behavior-based regrouping moved two students in each LLM condition between guided and unguided categories.
• Practice time was self-reported and transcript coding required human judgment despite five graders, and the Guided-LLM group received additional training and reminders that may have introduced attention, expectancy, or demand effects.
• The study ran as a single four-week summer program in one undergraduate Probability and Statistics course at one university, so transfer to full-semester courses and other disciplines is untested.

Citation

Amanlou, M., Amou-Jafari, Y., Livani, M., Boloukazari, F., Bagheri, F., & Bahrak, B. (2026). Beyond Access: Guided LLM Scaffolding for Independent Learning in Undergraduate Statistics. Proceedings of the 34th International Conference on Computers in Education.