Related papers: Framework for Adoption of Generative Artificial Intelligence (GenAI) in Education

Framework for Adoption of Generative Artificial Intelligence (GenAI) in Education

URL: http://arxiv.org/abs/2408.01443v1
Date: Sun, 21 Jul 2024 12:02:28 GMT
Title: Framework for Adoption of Generative Artificial Intelligence (GenAI) in Education
Authors: Samar Shailendra, Rajan Kadel, Aakanksha Sharma,
Abstract summary: This study focuses on examining the university education system and assessing the opportunities and challenges related to incorporating GenAI in teaching and learning. It identifies a gap and the absence of a comprehensive framework that obstructs the effective integration of GenAI within the academic environment.
Score: 0.032771631221674334
License: http://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract: Contributions: An adoption framework to include GenAI in the university curriculum. It identifies and highlights the role of different stakeholders (university management, students, staff, etc.) during the adoption process. It also proposes an objective approach based upon an evaluation matrix to assess the success and outcome of the GenAI adoption. Background: Universities worldwide are debating and struggling with the adoption of GenAI in their curriculum. Both the faculty and students are unsure about the approach in the absence of clear guidelines through the administration and regulators. This requires an established framework to define a process and articulate the roles and responsibilities of each stakeholder involved. Research Questions: Whether the academic ecosystem requires a methodology to adopt GenAI into its curriculum? A systematic approach for the academic staff to ensure the students' learning outcomes are met with the adoption of GenAI. How to measure and communicate the adoption of GenAI in the university setup? Methodology: The methodology employed in this study focuses on examining the university education system and assessing the opportunities and challenges related to incorporating GenAI in teaching and learning. Additionally, it identifies a gap and the absence of a comprehensive framework that obstructs the effective integration of GenAI within the academic environment. Findings: The literature survey results indicate the limited or no adoption of GenAI by the university, which further reflects the dilemma in the minds of different stakeholders. For the successful adoption of GenAI, a standard framework is proposed i) for effective redesign of the course curriculum, ii) for enabling staff and students, iii) to define an evaluation matrix to measure the effectiveness and success of the adoption process.

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