arXiv:2605.30151v1 Announce Type: new Abstract: As AI tools become increasingly integrated into educational contexts, questions arise about both their stability over time and their responsiveness to prompt engineering techniques. This longitudinal study focused on different AI tools' ability to use the Task Analysis Guide (TAG; Stein \& Smith, 1998) to classify the cognitive demand of mathematics tasks. In particular, it examined whether this classification ability changed with (1) model version updates over time and (2) few-shot prompting using exemplar tasks. We tested a general-purpose AI t
The rapid integration of AI tools into educational and professional contexts necessitates ongoing assessment of their reliability and adaptability.
This research provides critical insights into the stability and adaptability of AI models, which are fundamental to their trustworthy deployment in sensitive applications like education.
The understanding of how AI tools' assessment capabilities evolve over time and respond to prompt engineering in specific domains like mathematics is enhanced.
- · AI developers
- · Educational technology sector
- · Educators implementing AI
- · Over-reliant AI users
- · Stagnant AI models
Increased scrutiny and demand for robust, stable AI models in specialized applications.
Development of standardized testing and validation procedures for AI across different domains.
Formalized regulatory frameworks for AI deployment in high-stakes environments, potentially including educational certifications.
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Read at arXiv cs.AI