FUTURE-READY FACULTY: EMBRACING GENERATIVE ARTIFICIAL INTELLIGENCE (GENAI) IN HIGHER EDUCATION

Authors

  • Dr. Aisha Ismail Virtual University of Pakistan
  • Sadaf Choudhary Virtual University of Pakistan

DOI:

https://doi.org/10.36261/ijdeel.v11i2.7292

Abstract

Artificial intelligence (AI) in higher education (HE) is becoming a prominent area for researchers to explore. Perceived benefits of generative AI in HE includes adaptive learning, personalized pathways, quality content, student engagement, customized and prompt feedback, and intelligent tutoring systems.  These benefits can be achieved if educators apply generative AI in their teaching in the right way; therefore, it is critical to explore the readiness of faculty to use AI. This study aims to explore the extent to which faculty members are ready to use AI in their teaching. Furthermore, identifying the factors affecting AI-readiness and behavioral-intention of faculty at open and distance learning institutions (ODL) is also a focus of this study. Using a quantitative research method, the study has collected data from ODL institution in Pakistan. Variables, including AI anxiety, confidence in teaching AI, AI relevance, AI for social good, attitude of using AI and perceived usefulness and AI-readiness of ODL faculty have been examined to measure their behavioral intentions towards using AI in their pedagogy. The online survey was sent to 200 faculty members of ODL from which 180 correctly filled questionnaires were received. Findings indicate that confidence in teaching AI, AI for social good, and relevance are the predictor of AI-Readiness whereas AI-Readiness is a good predictor of behavioral intention of ODL faculty members. This paper contributes to AI literature in higher education by emphasizing the AI benefits specifically in ODL through its faculty. The findings are helpful for higher education institutions (HEIs) to know the factors contributing towards readiness of ODL faculty to integrate AI in higher education, so that policies to adopt AI in education can be formulated. The findings are also important for apex bodies in higher education to identify the areas to be focused on for an effective use of AI.

Key Words: AI, Generative AI, Behavioral Intention, Adaptive Learning, Readiness to use AI, Attitude to use AI, Open and Distance Learning (ODL)

Author Biographies

Dr. Aisha Ismail, Virtual University of Pakistan

Dr. Aisha Ismail is serving Virtual University of Pakistan as a Faculty-Accounting & Finance. She has obtained her master’s degree in business administration with majors in Finance from IBA, University of the Punjab Lahore and B.Com (IT) from Punjab college of Commerce. Her areas of interest are Generative AI, Microfinance, Corporate finance, Investment analysis and Portfolio management, Risk management and Open and Distance Learning (ODL)-teaching practices.

Sadaf Choudhary, Virtual University of Pakistan

Sadaf Chouhdary has been serving Virtual University (VU) of Pakistan as a Course Instructor Management/HRM since March, 2008. She has obtained MS degree in Entrepreneurship & SME (Small & Medium Enterprise) from Government College University (GCU) Lahore in 2013, Master’s degree in Business Administration (MBA) with Majors in Human Resource Management (HRM) in 2007 from COMSATS Institute of Information & Technology, and completed BBIT (hons) from Lahore College for Women University (LCWU) in 2005. Her research areas are Artificial Intelligence (AI), Open and Distance Learning (ODL), Knowledge Management, Human Resource Management and Organizational Culture.

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Published

2026-06-29

Issue

Vol. 11 No. 2 (2026): International Journal of Distance Education and E-Learning (IJDEEL)

Section

Peer-Reviewed Articles

License

© By Department of Education (Directorate of Distance Education) International Islamic University Islamabad. IJDEEL by International Islamic University Islamabad is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Based on a work at www.iiu.edu.pk.  All articles published by IJDEEL are licensed under the Creative Commons Attribution- NonCommercial 4.0 International License . This permits anyone to copy, redistribute, transmit and adapt the work provided the original work and source is appropriately cited as specified by the Creative Commons Attribution-NonCommercial 4.0 International License.