AI-Powered Tutoring for Active Reading: A Pedagogical and Ethical Approach
Bibliographic record
Abstract
In higher education, reading proficiency is essential for academic success. Yet, many students begin postsecondary studies with underdeveloped reading skills, limiting both knowledge acquisition and disciplinary engagement. This paper presents the design, implementation, and preliminary evaluation of an AIbased tutoring system for active reading, adapted from a speech therapy technology, called TutorIAt. Our system leverages speech recognition to guide students through critical reading processes, fosters self-regulated learning, and complements existing academic support services. We also discuss copyright management, ethical safeguards, and equitable use guidelines developed with faculty collaboration to ensure responsible deployment. Finally, we illustrate how the system engages students in using effective reading strategies as it provides feedback and phonetic analysis in less than 3 seconds after reading. Preliminary results show that such a system can promote more equitable, inclusive, and autonomous access to success in higher education.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".