THE IMPACT OF ADAPTIVE AI TUTORING SYSTEMS ON STUDENT LEARNING OUTCOMES IN ECONOMICS: A COMPARISON OF PERSONALIZED PATHWAYS
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2026
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| author | IBETO, MICHAEL UCHENNA |
| author_facet | IBETO, MICHAEL UCHENNA |
| contents | <p class="MsoNormal"><strong><span>ABSTRACT</span></strong></p> <p class="MsoNormal"><em><span>The use of artificial intelligence in higher education represents a paradigm shift in pedagogy, from traditional one-size-fits-all approaches to teaching towards adaptive and personalized learning experiences. This study investigated </span></em><em><span>and evaluates the efficacy of AI-driven tutoring platforms in improving the academic achievements of university economics students. Employing a hybrid methodology, the study contrasts student achievement, involvement, and satisfaction in conventional lecture formats against personalized, AI-supported learning settings across a full semester.</span></em><em><span> The research utilized machine learning algorithms to map individual learning trajectories, knowledge gaps, and most efficient content delivery modes in 200 undergraduate economics students. Data sources include pre- and post-test assessments, learning analytics from the AI system, student questionnaire surveys, and qualitative interviews. The study design examined the ways adaptive AI systems adapt content difficulty, pace, and pedagogy based on real-time student performance data. Expected outcomes include increased learning efficiency, enhanced student engagement, and improved academic performance through personalized learning pathways. The findings of the research suggested that AI can redefine pedagogical practice in higher education generally, and in the area of economics education in particular, while coping with challenges that could emerge through digital divide, data privacy, and teachers' roles in AI-aided learning environments.</span></em></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19645457 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | THE IMPACT OF ADAPTIVE AI TUTORING SYSTEMS ON STUDENT LEARNING OUTCOMES IN ECONOMICS: A COMPARISON OF PERSONALIZED PATHWAYS IBETO, MICHAEL UCHENNA <p class="MsoNormal"><strong><span>ABSTRACT</span></strong></p> <p class="MsoNormal"><em><span>The use of artificial intelligence in higher education represents a paradigm shift in pedagogy, from traditional one-size-fits-all approaches to teaching towards adaptive and personalized learning experiences. This study investigated </span></em><em><span>and evaluates the efficacy of AI-driven tutoring platforms in improving the academic achievements of university economics students. Employing a hybrid methodology, the study contrasts student achievement, involvement, and satisfaction in conventional lecture formats against personalized, AI-supported learning settings across a full semester.</span></em><em><span> The research utilized machine learning algorithms to map individual learning trajectories, knowledge gaps, and most efficient content delivery modes in 200 undergraduate economics students. Data sources include pre- and post-test assessments, learning analytics from the AI system, student questionnaire surveys, and qualitative interviews. The study design examined the ways adaptive AI systems adapt content difficulty, pace, and pedagogy based on real-time student performance data. Expected outcomes include increased learning efficiency, enhanced student engagement, and improved academic performance through personalized learning pathways. The findings of the research suggested that AI can redefine pedagogical practice in higher education generally, and in the area of economics education in particular, while coping with challenges that could emerge through digital divide, data privacy, and teachers' roles in AI-aided learning environments.</span></em></p> |
| title | THE IMPACT OF ADAPTIVE AI TUTORING SYSTEMS ON STUDENT LEARNING OUTCOMES IN ECONOMICS: A COMPARISON OF PERSONALIZED PATHWAYS |
| url | https://doi.org/10.5281/zenodo.19645457 |