AI-Enabled Teaching and Student Learning Quality in Higher Education: Personalization, Engagement, and Academic Integrity

Manju

AI-Enabled Teaching and Student Learning Quality in Higher Education: Personalization, Engagement, and Academic Integrity

Author(s)	Dr. Manju
Country	India
Abstract	The use of the Artificial intelligence (AI)-based instructional approaches in higher education has become a rapidly growing phenomenon. AI is being applied in universities in the following ways: adaptive learning, automated feedback, chatbots, intelligent tutoring, predictive analytics, and generative tools that can assist with content creation, explanation, and assessment preparation. In this paper, the author will discuss the effect of these AI-based modes of instruction on the quality of learning of students in institutions of higher learning. The concept of learning quality, in this provision, is a multidimensional phenomenon, which involves conceptual knowledge, interaction, timely feedback, self-regulated learning, critical thinking, academic achievement, and preparation to address real-life problems. The paper is of the view that AI can make learning much better in case it is implemented in the framework of good pedagogy, teacher coaching and ethical institutional policy. Recent systematic reviews demonstrate that AI could be used to facilitate personalization, improve student engagement, and make the instruction more responsive, especially when working with large and diverse classes. Nevertheless, some key issues, such as the excessive use of AI, academic dishonesty, algorithmic bias, lack of equal access, risks to data privacy, and the risk of superficial learning, are also mentioned in the literature. The paper also formulates research objectives and hypotheses, summarizes the recent literature and explains the pedagogical and institutional conditions in which AI produces the most positive impact. The results indicate that AI is not necessarily more likely to enhance the results of higher education, but its usefulness is conditional on its designing, governance, and implementation into the assessment and teaching processes. This paper concludes that an unbiased, morally controlled and evaluation conscious AI integration can be applied in the most effective way to achieve better quality of learning without compromising the critical thinking, equity and academic integrity in higher education establishment.
Keywords	Artificial intelligence in education; higher education; quality of learning and teaching methods based on AI; student engagement.
Published In	Volume 17, Issue 1, January-March 2026
Published On	2026-03-23

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AI-Enabled Teaching and Student Learning Quality in Higher Education: Personalization, Engagement, and Academic Integrity

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