Disclosures & Disclaimers: Investigating the Impact of Transparency Disclosures and Reliability Disclaimers on Learner-LLM Interactions
Bibliographic record
Abstract
Large Language Models (LLMs) are increasingly being used in educational settings to assist students with assignments and learning new concepts. For LLMs to be effective learning aids, students must develop an appropriate level of trust and reliance on these tools. Misaligned trust and reliance can lead to suboptimal learning outcomes and reduced LLM engagement. Despite their growing presence, there is a limited understanding of achieving optimal transparency and reliance calibration in the educational use of LLMs. In a 3x2 between-subjects experiment conducted in a university classroom setting, we tested the effect of two transparency disclosures (System Prompt and Goal Summary) and an in-conversation Reliability Disclaimer on a GPT-4-based chatbot tutor provided to students for an assignment. Our findings suggest that disclaimer messages included in the responses may effectively mitigate learners' overreliance on the LLM Tutor in the presence of incorrect advice. Disclosing System Prompt seemed to calibrate students’ confidence in their answers and reduce the occurrence of copy-pasting the exact assignment question to the LLM tutor. Student feedback indicated that they would like transparency framed in terms of performance-based metrics. Our work provides empirical insights on the design of transparency and reliability mechanisms for using LLMs in classrooms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".