Challenges and Horizons of Artificial Intelligence in Cognitive Assessment and Educational Test Development: A Constraint-Satisfaction Conceptual Model

Repeated cognitive and educational assessment is indispensable in longitudinal research, in the evaluation of instructional and rehabilitation programmes, and in high-stakes testing. Yet re-administering the same items inflates scores through practice effects, exposes item content, and progressively erodes the validity of the measurement. Parallel forms are the classical remedy, but their manual construction is slow and costly, so most cognitive instruments never acquire one. This paper argues that the bottleneck is not conceptual but procedural, and that it can be dissolved by treating item construction as a formal search problem. We present an analytical model in which the construction of an item for a rule-governed cognitive task is expressed as a constraint satisfaction problem defined over shape variables, latent weights and finite domains, and governed by five psychometrically motivated constraints: balance, difficulty, error-based distractors, non-repetition, and chain necessity. A generator combining backtracking search with arc-consistency propagation is described, together with an operational difficulty function that encodes working memory load, mapping load, and inferential depth. The distinguishing property of the approach is that item quality is guaranteed structurally at construction time rather than verified after the fact, and that every generated item carries a fully auditable derivation. We then examine four horizons this capability opens for educational assessment (dynamic item banking and adaptive testing, test security in high-stakes examinations, unbiased measurement of change in instructional interventions, and diagnostic formative assessment) and close with the technical, psychometric and ethical limits that a symbolic generator must respect before it can be trusted with consequential decisions.

کلیدواژه ها:

Automatic item generation ، Constraint satisfaction ، Parallel forms ، Educational assessment ، Symbolic artificial intelligence