Journal of Information Technology Case and Application Research

AI Innovation in Business Education

Theme: AI Innovation in Business Education

The rapid evolution of artificial intelligence is poised to fundamentally reshape the landscape of business education. This special issue invites research that explores the multifaceted impact of AI on business education, from pedagogical innovations and curriculum design to the ethical and societal implications of an AI-driven educational ecosystem. We are particularly interested in how AI is transforming the core functions of business schools, including teaching, learning, research, and administration. The rise of AI-powered personalized learning platforms, automated assessment tools, and virtual teaching assistants necessitates a critical examination of their effectiveness, fairness, and impact on the student experience. Furthermore, the integration of AI into the business world demands a rethinking of what it means to be an "AI-literate" business graduate and how curricula can be adapted to foster the skills the future workforce will need. By focusing on the convergence of AI, pedagogy, and institutional strategy, this special issue seeks to bring together a diverse range of perspectives to foster a nuanced understanding of this paradigm shift. 

Research conducted using case studies, experiments, surveys, simulations, and any other appropriate methodology is welcome.

Suggested Topics for the Special Issue

We invite original submissions that address, but are not limited to, the following topics:

• AI-enabled pedagogical innovation in business disciplines
  • Applications of generative AI in core business subjects (e.g., accounting, finance, marketing, strategy).
  • Human-AI collaboration in case study analysis, simulations, and experiential learning.
  • Measuring the impact of AI tools on student engagement, collaboration, and critical thinking.
• The evolving role and expertise of business school faculty
  • Redefining the educator: transitioning from content deliverer to learning architect and AI mentor.
  • Overcoming resistance: adoption curves and cultural change among academic staff.
  • Faculty development and upskilling programs for effective AI integration.
• AI-driven transformation of administrative and student services
  • AI-powered tools for personalized student advising, career counseling, and wellness support.
  • Automation and algorithmic augmentation in admissions, enrollment, and recruitment.
  • Optimizing resource allocation, course scheduling, and business school management via AI.
• Redefining assessment, governance, and academic integrity, and the changing meaning of learning
  • Designing authentic, AI-resilient assessment strategies for business education.
  • The role of AI in providing scalable, formative feedback versus summative evaluation.
  • Business school policies and technological frameworks for upholding academic integrity.
  • Data privacy, security, and ownership in AI-driven educational platforms.
  • Addressing algorithmic bias and ensuring fairness in AI-based systems.
• Digital infrastructure and the AI-ready business school
  • Building the data and IT architecture required for scalable AI-powered business education.
  • Strategies for collecting, managing, and utilizing student data ethically.
  • The role of public-private partnerships in building AI infrastructure.
• Curriculum transformation and the AI-literate business graduate
  • Defining the core competencies, capabilities, and ethical grounding of an AI-literate business graduate.
  • Developing students’ capabilities to collaborate effectively with AI in managerial and professional work.
  • Integrating AI literacy and prompt engineering across the broader business curriculum.
  • The evolution of specialized curricula (e.g., Information Systems, Business Analytics) to lead AI integration.
• Strategic reinvention and business models of business education
  • The impact of AI on the financial models, revenue streams, and cost structures of business schools.
  • The emergence of new, AI-enabled educational products and executive education services.
  • Scenarios for the long-term strategic positioning and competitive landscape of business schools.
• AI, employability, and workforce readiness of business students
  • The impact of AI on the skills and competencies demanded by employers of business graduates.
  • How business schools can better prepare graduates for the AI-augmented workforce.
  • The future of career services and graduate placement in the age of AI.
  • Longitudinal studies on the career trajectories of graduates in an AI-driven economy.

References

·       Bao, W. (2020). COVID-19 and online teaching in higher education: A case study of Peking University. Human Behavior and Emerging Technologies, 2(2), 113-115.

·       Borenstein, J., & Howard, A. (2020). Emerging challenges in AI and the need for AI ethics education. AI and Ethics, 1(1), 61-65.

·       Eckhardt, T. C. (2026). Entrepreneurial learning in the era of generative artificial intelligence. Academy of Management Proceedings, 2026(1).

·       Grundmann, S., & Scheel, T. (2023). ChatGPT and comparable AI in higher education: A systematic review. Education and Information Technologies, 28(11), 15413-15456.

·       Heredia-Carroza, J., & Stoica, R. (2023). Artificial intelligence in higher education: A literature review. Journal of Public Administration, Finance and Law, (30), 105-117.

·       Kwok, R. C. W., Cheung, J. C. F., Chung, A. Y. K., Hu, W. Y., Sze, L. K. Y., Chan, C. K. Y., Ho, K. H., Law, M., Ng, G. M. S., Thadani, D. R., & Yeung, E. W. K. (2025). From learners to innovators: Enhancing entrepreneurship education through AI-powered team-based learning. Journal of Information Technology Case and Application Research, 27(2-3), 186-193.

·       Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity.

·       Xie, X., Siau, K., & Nah, F. F. H. (2020). COVID-19 pandemic – online education in the new normal and the next normal. Journal of Information Technology Case and Application Research, 22(3), 175–187.

·       Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators?  International Journal of Educational Technology in Higher Education, 16, 39.

Important Dates

·       Aug 15, 2026: Launch call for papers

·       Jan 15, 2027: Deadline for paper submission (online)

·       April 15, 2027: First-round decisions

·       June 15, 2027:  Deadline to submit revised papers (online)

·       July 15, 2027:  Second-round revisions

·       Aug 15, 2027:  Provisional/Final decisions

·       Sept 15, 2027: Deadline to submit final paper (if minor revision is required)