ISCAP Proceedings: Abstract Presentation
Teaching Self-Efficacy and Developing Students’ Self-Worth: What GenAI Can’t Do
Kevin Craig
University of North Carolina Wilmington
Abstract
Currently, there is considerable excitement about Generative Artificial Intelligence (GenAI) as a replacement for a wide range of human roles. However, for all its advantages, artificial intelligence is limited by one inescapable factor: it is free from the constraints of a human brain and body. As a result, it can never serve as a role model for students as they face the emotional challenges associated with learning. Moreover, attention from a GenAI can’t inform the self-worth of students. These facts have implications for Information Systems (IS) education. This research draws on works from philosophy, psychology, and mathematics to build a case that the limitations of human existence are not replicable by artificial intelligence, and that these limitations can offer advantages in IS pedagogy.
First, human IS instructors can’t be replaced in their ability to inform students’ self-efficacy for understanding and using information systems, especially in the domain of coding. Even before the advent of GenAI, the actual syntax of programming languages has been a trivial part of teaching people to code, as compared with teaching them how to think like a coder. We’ve all seen students freeze up and stop thinking as soon as they hit a snag while trying to solve problems with a computer. So, we teach troubleshooting and problem solving, and both of these behaviors depend on self-efficacy. When a student sees a human instructor troubleshoot code, they see what they, too, are capable of doing if they keep trying. This builds self-efficacy and tenacity. A GenAI, on the other hand, has perfect coding knowledge and its effort and success thus can’t inform a human student’s sense of what they, with imperfect knowledge, can do.
Second, there are important societal effects of human teaching that can’t be reproduced by GenAI instructors. When a student realizes that a mentor or teacher has sacrificed time and effort to teach and support them, they subconsciously perceive that sacrifice as evidence that they are valued. In this way, the student’s self-worth is elevated. GenAI applications do not value their own time and effort as humans do, and thus they have no notion of sacrifice, and their choice to invest time and effort is the product of an algorithm and not of free choice. So, attention from a GenAI instructor does not register in the student’s subconscious as evidence of their value to other human beings, and thus GenAI instruction does not elevate student self-worth.
These two points are among several that stem from a range of observations taken from philosophy and mathematics. Going forward, this research effort will produce a literature review that draws on the rules of formal rationality to explain what GenAI cannot and can never do.