| Title: |
AI in IS Education: A Categorization of ISCAP Publications Using Zuboff's Automate-Informate-Transformate Framework. |
| Authors: |
Sharp, Jason H.; Anderson, John E.; Lang, Guido |
| Source: |
Information Systems Education Journal; Jul2026, Vol. 24 Issue 4, p22-35, 14p |
| Subject Terms: |
ARTIFICIAL intelligence; INFORMATION technology education; EDUCATIONAL technology; GENERATIVE artificial intelligence; HIGHER education; CURRICULUM planning |
| Abstract: |
The rapid emergence of generative artificial intelligence tools such as ChatGPT, Copilot, and Gemini has sparked widespread interest in their pedagogical applications within higher education. This study investigates how Artificial Intelligence is being applied in Information Systems education by analyzing ISCAP publications--including the Journal of Information Systems Education, the Information Systems Education Journal, and ISCAP conference proceedings from 2022 to 2025. Using Zuboff's automate-informate-transformate framework, the authors employed both Microsoft Copilot and human raters to categorize 18 peer-reviewed papers that explicitly address AI in teaching and learning contexts. The results reveal that most applications fall within the "informate" category, emphasizing AI's role in enhancing understanding, reflection, and skill development. Fewer papers were categorized as "automate," where AI replaces human tasks, or "transformate," where AI fundamentally restructures educational practices. Findings suggest that while automation is often used by instructors to streamline tasks, the dominant pedagogical value of AI lies in its capacity to inform and scaffold learning. Transformative uses of AI, though limited, are emerging and may signal future shifts in instructional design and learner engagement. This paper contributes to the growing discourse on AI in education by providing a structured lens for evaluating its impact and providing examples of its use. [ABSTRACT FROM AUTHOR] |
| : |
Copyright of Information Systems Education Journal is the property of Information Systems & Computing Academic Professionals (ISCAP) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Complementary Index |