Autors
Antonella Nuzzaci.
Abstract
The integration of AI into university diagnostic assessment often raises fears of dehumanizing pedagogy. This study analyzes the transition from traditional evaluative models to AI-assisted ones, identifying faculty training as the strategic lever for change. Diagnostic AI is interpreted as a support tool for the early identification of learning gaps and the prevention of academic dropout through predictive analytics. The contribution explores training models designed to reconfigure the perception of AI from a “threatening agent” to a “cognitive and affective scaffold”. By promoting digital literacy and overcoming technophobia, the research demonstrates how a favorable disposition of university professors toward the use of diagnostic assessment can transform initial data into elements capable of improving teaching-learning processes. The study highlights how AI allows university professors to focus more consciously on pedagogical relationship and interaction, ensuring a closer alignment of instruction with students’ formative needs.
Keywords
Faculty readiness; Diagnostic assessment; Higher education.
Bibliografia
Hussain, M., Zhu, W., Zhang, W., & Abidi, S. M. R. (2018). Previsioni sull’impegno degli studenti in un sistema e-Learning e il loro impatto sui punteggi delle valutazioni dei corsi degli studenti.
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intelligence applications in higher education – where are the educators?. Int J Educ Technol High
Educ 16, 39 (2019). https://doi.org/10.1186/s41239-019-0171-0
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