Generative AI Solutions for Faculty and Students: A Review of Literature and Roadmap for Future Research

Giulio Marchena Sekli, Amy Godo, José Carlos Véliz
Journal of Information Technology Education: Research  •  Volume 23  •  2024  •  pp. 014

This paper aims to address the gap in comprehensive, real-world applications of Generative Artificial Intelligence (GenAI) in education, particularly in higher education settings. Despite the evident potential of GenAI in transforming educational practices, there is a lack of consolidated knowledge about its practical effectiveness and real-world impact.

This study addresses this gap by conducting a systematic literature review to collate and analyze real-life instances of GenAI applications in higher education, thus providing a nuanced understanding of its practical implementations and measurable outcomes.

The paper utilizes a systematic literature review methodology, adopting the PRISMA approach complemented by a thematic analysis procedure to ensure a comprehensive and in-depth evaluation of the literature. It synthesizes information from relevant articles from 2022 to 2024, focusing on the applications of GenAI in higher education. This analysis covers various aspects, including research settings, analysis scales, data types, collection tools, and analytical methods.

The paper contributes to the academic community by offering a comprehensive review of GenAI applications in education, highlighting the current precision level of these tools, and providing strategic recommendations for their effective use in academia. Furthermore, the research defines seven specific cases where Gen AI can be utilized as a reference for educational institutions in their adoption strategies.

Key findings include the versatility of GenAI in generating teaching materials, enhancing skill development, supporting student tasks, academic performance evaluation, feedback delivery, and its role as a virtual assistant and in research support.

Practitioners are advised to explore the integration of GenAI for diverse educational purposes, from content creation to student assessment, while being cognizant of its limitations and ethical considerations.

Future research should focus on addressing the gaps identified, such as the implications of GenAI in research roles, its application in various disciplines, and the exploration of newly developed AI tools tailored to specific educational needs.

The findings of this paper highlight the potential of GenAI in revolutionizing the educational sector, offering personalized learning experiences, and significantly influencing teaching methodologies and student engagement, but it also reveals significant deficiencies of Generative AI, known as hallucinations, which can impact the expected results.

Subsequent research should explore the evolving capabilities of GenAI models, their impact on various academic disciplines, and the development of pedagogical strategies to optimize their use in education.

generative AI, education, systematic literature review, teaching materials, skill development, academic performance
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