Exploring the Impact of Generative AI on Student Engagement in Higher Education

A special issue of Education Sciences (ISSN 2227-7102). This special issue belongs to the section "Technology Enhanced Education".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 1467

Special Issue Editor


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Guest Editor
University Online Education Services, Rutgers University, New Brunswick, NJ 08901-8554, USA
Interests: faculty development with generative AI; student engagement; technological affordances

Special Issue Information

Dear Colleagues,

Augmenting student engagement has been a goal of higher education for decades. The proliferation of Generative AI has been a game changer in enhancing student engagement in digital learning environments. Exceeding the capabilities of traditional academic support, GenAI has introduced possibilities to enrich student engagement by providing dynamic support in the form of AI-powered tutoring systems that facilitate personalized learning, adaptive learning platforms, and assistive technologies that provide real-time support.

For higher education faculty, GenAI can analyze patterns of engagement with online course content, shedding light on possible areas of improvement and opportunities to facilitate personalized learning. Faculty can plan for early intervention strategies based on data analysis by AI tools that identify students who may be struggling. GenAI can also assist faculty with designing inclusive and accessible learning environments, thereby enhancing engagement from diverse groups of learners. 

GenAI has shown potential in improving students’ historically low engagement with feedback. This is made possible by providing opportunities for students to interact with tools in distinctive engagement patterns that require them to think, feel, and act differently compared to their responses to feedback from humans. 

This editorial introduction to the Special Issue titled “Exploring the Impact of Generative AI on Student Engagement in Higher Education”, for the journal Education Sciences, invites articles and studies that discuss the potential of GenAI to elevate student engagement in online learning within higher education, while also considering ethical, instructional, and empirical challenges.

Dr. Suparna Sinha
Guest Editor

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Keywords

  • GenAI
  • student engagement
  • faculty development

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Published Papers (2 papers)

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Research

24 pages, 779 KB  
Article
From Expectations to Measured Pragmatism: A Pre- and Post-Experience Study of Student Engagement in AI-Supported Academic Exams
by Meital Amzalag, Rina Zviel-Girshin and Dizza Beimel
Educ. Sci. 2026, 16(4), 642; https://doi.org/10.3390/educsci16040642 - 17 Apr 2026
Viewed by 178
Abstract
Generative AI (GenAI) is transforming higher education assessments, yet empirical research on students’ lived experiences with GenAI during graded, time-constrained classroom assessments remains scarce. This study investigates how direct experience with GenAI in examinations shapes student perceptions of learning, metacognition, and engagement. Drawing [...] Read more.
Generative AI (GenAI) is transforming higher education assessments, yet empirical research on students’ lived experiences with GenAI during graded, time-constrained classroom assessments remains scarce. This study investigates how direct experience with GenAI in examinations shapes student perceptions of learning, metacognition, and engagement. Drawing on self-regulated learning research and cognitive load theory, we employed a retrospective pre–post design to analyze qualitative reflections and quantitative data from 90 undergraduate computer science and engineering students. Our qualitative analysis suggests a complex recalibration from idealized expectations of efficiency toward what may be described as a state of measured pragmatism. Interpretive analysis of Post-experience reflections indicates that direct practical engagement appeared to make students more conscious of the need for metacognitive engagement, with a focus on real-time output verification and the restrictive role of time pressure. Concerns regarding assessment authenticity and fairness emerged only after direct engagement. Quantitative results show that although 68.5% preferred the GenAI format, this preference did not correlate significantly with academic performance (r = 0.014, p = 0.89). Those findings suggest that student engagement is driven by pedagogical and professional relevance rather than grade improvement alone. Overall, the findings underscore the need for assessment designs that balance cognitive support with active student monitoring and responsibility. Full article
12 pages, 3222 KB  
Article
Integrating Generative AI in Engineering Education: Enhancing Learning and Attendance in a Vehicle Theory Course
by Fernando Viadero-Monasterio, Ramón Alberto Gutiérrez-Moizant, Miguel Meléndez-Useros and Daniel García-Pozuelo Ramos
Educ. Sci. 2026, 16(2), 239; https://doi.org/10.3390/educsci16020239 - 3 Feb 2026
Viewed by 647
Abstract
This paper presents the results of a two-year innovative teaching project in the Vehicle Theory course, a fourth-year Mechanical Engineering subject at Universidad Carlos III de Madrid. The project explored the integration of generative artificial intelligence (GenAI) tools, particularly ChatGPT, to enhance student [...] Read more.
This paper presents the results of a two-year innovative teaching project in the Vehicle Theory course, a fourth-year Mechanical Engineering subject at Universidad Carlos III de Madrid. The project explored the integration of generative artificial intelligence (GenAI) tools, particularly ChatGPT, to enhance student engagement, support project work, and promote ethical academic use. Key strategies included a flipped classroom approach, where students summarized previous lessons with GenAI assistance, and the use of AI to aid in the design and optimization of a tubular chassis project. Survey results and course observations indicate high student adoption of GenAI, with positive impacts on understanding theoretical concepts, completing exercises, and generating project outputs. Students reported that GenAI facilitated idea generation, technical problem-solving, and the creation of more effective and visually appealing presentations. Limitations included information bias, overreliance on GenAI, and variability in response quality depending on prompt formulation. Overall, the project improved attendance, engagement, and academic performance, highlighting the potential of GenAI as a complementary educational tool. Additionally, by requiring students to critically evaluate the GenAI responses, the project encouraged the development of judgment and decision-making skills, which are essential competences for future engineers. Full article
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