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Research on Teaching Quality Evaluation of Interactive Mobile AI Smart Classrooms Based on AHP.

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Title: Research on Teaching Quality Evaluation of Interactive Mobile AI Smart Classrooms Based on AHP.
Authors: Liu, Quan1, Guan, Jingjing1 18971083933@163.com, Efendiev, Rakib2
Source: International Journal of Interactive Mobile Technologies. 2026, Vol. 20 Issue 11, p126-137. 12p.
Subjects: Analytic hierarchy process, Evaluation methodology, Educational objectives, Effective teaching, Student-centered learning, Educational technology, Experiential learning
Abstract: The interactive mobile artificial intelligence (AI) smart classroom is a new teaching model that deeply integrates mobile terminals, AI, and classroom instruction. However, current teaching quality evaluations face challenges such as high subjectivity and vague indicators. To address these issues, this paper selects five evaluation factors: teaching objectives, interactive teaching processes, AI application, learning experience, and teaching effectiveness. The analytic hierarchy process (AHP) is first utilized to determine the weight of each indicator, followed by the Fuzzy comprehensive evaluation (FCE) method for integrated assessment. This study selects three representative smart teaching courses offered in universities as empirical samples for analysis. The results indicate that Research Methods in Educational Technology scored 86.77, Python Programming scored 86.29, and Design and Development of Smart Courses scored 90.86. Overall, the performance ranges from "Good" to "Excellent." These findings provide a quantitative basis for teachers to optimize instruction and for institutions to improve management levels. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
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Abstract:The interactive mobile artificial intelligence (AI) smart classroom is a new teaching model that deeply integrates mobile terminals, AI, and classroom instruction. However, current teaching quality evaluations face challenges such as high subjectivity and vague indicators. To address these issues, this paper selects five evaluation factors: teaching objectives, interactive teaching processes, AI application, learning experience, and teaching effectiveness. The analytic hierarchy process (AHP) is first utilized to determine the weight of each indicator, followed by the Fuzzy comprehensive evaluation (FCE) method for integrated assessment. This study selects three representative smart teaching courses offered in universities as empirical samples for analysis. The results indicate that Research Methods in Educational Technology scored 86.77, Python Programming scored 86.29, and Design and Development of Smart Courses scored 90.86. Overall, the performance ranges from "Good" to "Excellent." These findings provide a quantitative basis for teachers to optimize instruction and for institutions to improve management levels. [ABSTRACT FROM AUTHOR]
ISSN:18657923
DOI:10.3991/ijim.v20i11.61825