Assessment and Artificial Intelligence: From Traditional Testing to the Intelligent Portfolio

Educational assessment has undergone profound transformation in recent years. The emergence of artificial intelligence has pushed the boundaries of traditional assessment and opened new horizons for alternative forms of evaluation. This article, using a qualitative-descriptive approach, examines the impact of artificial intelligence on the shift from traditional standardized testing to the intelligent portfolio. The theoretical foundations of the article are based on authentic assessment theory, personalized learning theory, and distributed intelligence theory. The literature suggests that traditional assessment systems, due to their emphasis on recall and lower-order skills, are incapable of measuring twenty-first-century competencies, and that artificial intelligence can facilitate the transition away from this model. The research employed a qualitative-descriptive method based on a systematic review of the literature and analysis of empirical evidence. The findings were organized into five main themes: a critique of traditional assessment systems and the dominance over standardized testing; an introduction to alternative assessment and its relationship with artificial intelligence; practical applications of AI in assessment, ranging from automated scoring to multimodal content analysis; the intelligent portfolio as an advanced model of AI-supported alternative assessment; and the ethical challenges and requirements of implementation. The results indicate that intelligent assessment, by valuing human agency and adhering to the principles of transparency, fairness, and privacy, can facilitate educational transformation.

کلیدواژه ها:

artificial intelligence in education ، alternative assessment ، smart portfolio