AI-Driven Learning Analytics for Applied Behavior Analysis Therapy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Applied Behavior Analysis (ABA) therapy is a widely used intervention for students with special education needs (SEN), particularly those with Autism Spectrum Disorder (ASD) and co-occurring intellectual disabilities. However, despite its proven effectiveness, the integration of artificial intelligence (AI) and learning analytics (LA) in ABA therapy remains largely underexplored. This study examines the impact of an AI-driven learning analytics system on prediction performance, intervention effectiveness for SEN students, and support for therapists and teachers. The system collects and analyzes physiological, environmental, and behavioral data in real time to generate personalized intervention recommendations. A total of 33 students and 26 therapists/teachers from special schools and therapy centers in Hong Kong participated in an eight-week ABA intervention, followed by a post-evaluation session. The study assessed predictive accuracy, student learning outcomes, and educator perceptions using empirical data and qualitative feedback. Results indicate that the system achieved a predictive accuracy of 88.83% and a precision of 86.64% in forecasting learning outcomes, with statistically significant student performance improvement (medium effect size). Educators reported that the system's AI-driven recommendations enhanced their ability to develop individualized student profiles and intervention strategies. While the system did not replace traditional ABA methodologies, it improved decision-making by providing actionable insights through multimodal data integration. As of today, our system has been used by over 1,000 students with SEN in Hong Kong, Singapore, and Canada, demonstrating the real-world impact of AI-driven LA.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| 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 it