Building Trust in Educational AI: Designing Effective Human-AI Interfaces for Students and Instructors
Bibliographic record
Abstract
Integrating artificial intelligence (AI) in education presents unique challenges in balancing automated assistance with the need for trustworthy and personalized student support. Existing AI-driven learning tools often struggle with accuracy, fostering student mistrust, and creating additional oversight demands on instructors. To address these challenges, we present Anytime Question Hub, a system designed to provide students with immediate AI-generated answers to asynchronous questions while incorporating human-in-the-loop workflows for instructor oversight. The platform ensures all queries are resolved through a combination of AI responses and instructor verification, offering tools that minimize cognitive and administrative burden for educators. Deployment in a computer science course with 225 students highlights its potential to enhance student engagement and trust while reducing repetitive communication tasks for faculty. Our findings underscore the importance of integrating transparent, non-intrusive human-AI collaboration to support robust and reliable automated assistance in educational settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".