A Theory-Based Evaluation Framework for AI Language Learning Apps: An Evaluation of Inaya AI
Location
SU-215
Start Date
1-5-2026 12:00 PM
Department
Teaching English to Speakers of Other Languages
Abstract
This current study employs a criteria-based evaluation approach to examine the pedagogical design and learning affordances of INAAYA, an AI-powered language tutoring application. The primary objective of this methodology is to assess the app’s alignment with contemporary second language acquisition (SLA) theories, Computer-Assisted Language Learning (CALL) principles, and emerging expectations surrounding AI-mediated learning environments. Given INAAYA’s absence of a teacher and its reliance on generative AI output, personalization, and adaptive sequencing, a conventional CALL evaluation model proves inadequate. Consequently, this study adopts a hybrid framework that integrates established SLA-grounded CALL criteria with newly developed criteria specifically designed for AI-based language learning tools.
Faculty Sponsor
Ulugbek Nurmukhamedov
A Theory-Based Evaluation Framework for AI Language Learning Apps: An Evaluation of Inaya AI
SU-215
This current study employs a criteria-based evaluation approach to examine the pedagogical design and learning affordances of INAAYA, an AI-powered language tutoring application. The primary objective of this methodology is to assess the app’s alignment with contemporary second language acquisition (SLA) theories, Computer-Assisted Language Learning (CALL) principles, and emerging expectations surrounding AI-mediated learning environments. Given INAAYA’s absence of a teacher and its reliance on generative AI output, personalization, and adaptive sequencing, a conventional CALL evaluation model proves inadequate. Consequently, this study adopts a hybrid framework that integrates established SLA-grounded CALL criteria with newly developed criteria specifically designed for AI-based language learning tools.