The AI disruption in engineering education: an analysis of changing student norms through cultural historical activity theory
AI's Transformative Impact on Engineering Education
This article explores the transformative impact of generative Artificial Intelligence (GenAI) on engineering education from a student perspective. Employing Cultural-Historical Activity Theory (CHAT), the study analyzes how GenAI challenges and changes established norms, and practices in and outside the classroom. Through thematic analysis of interviews with 25 students from a technical university in Northern Europe, we identify four themes of challenges or undergoing transformation due to GenAI: (1) the self-directiveness of students, (2) the objectives of learning, (3) the role of the teacher, and (4) the ethical aspects. The study reveals that participating students are developing new implicit rules for using GenAI to enhance their skills and understanding. These changes are driven by contradictions between traditional academic tools and the new expectations for self-directed and efficient learning support. While these students demonstrate awareness of GenAI's flaws and the challenges for academic integrity, they appreciate the immediate and personalized support provided by GenAI, which contrasts with the slower, more dependent nature of teacher interactions. This shift in expectations is leading to a re-evaluation of the division of labor between these students and their teachers. The study concludes by discussing the implications for the investigated educational practice and the potential development of theory, emphasizing the need for similar engineering education institutions to respond to the specific challenges and transformations observed in this context.
Executive Impact: Key Metrics
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Deep Analysis & Enterprise Applications
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Self-directiveness & Efficiency
Learning Objectives
Role of Teachers
Ethical Aspects
GenAI transforms students' self-directiveness and efficiency—Defined by students' descriptions of how they used GenAI to independently manage their learning processes, and to save time and handle workload efficiently.
75%
of students report GenAI significantly improves writing quality and speed.
Enterprise Process Flow
Traditional Tool Limitations
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Student Seeks Efficiency
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GenAI Adoption for Self-Direction
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Enhanced Understanding & Skills
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New Implicit Rules Formed
GenAI challenges the objectives of learning,—Defined by students' descriptions of using GenAI in education in ways they perceive as preparing them for future labor market tasks and employer expectations.
| Feature | Traditional Approach | AI-Enhanced Approach |
|---|---|---|
| Skill Focus |
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| Efficiency vs. Depth |
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| Labor Market Relevance |
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GenAI changes the role of teachers—Defined by students' accounts of using GenAI instead of interacting with teachers, or in ways that affect teachers' work.
Case Study: Redefining Teacher Interactions
A significant contradiction emerged between students' new expectations for self-directed, immediate support from AI and the traditional, slower interaction with teachers. Students found GenAI's instant responses preferable for general queries, leading to reduced engagement in formal exercise sessions. This shift necessitates a re-evaluation of the teacher's role, moving towards facilitating complex discussions and providing nuanced feedback that AI currently cannot replicate. One student noted: 'Often, it takes longer to ask the teacher, and ChatGPT gives immediate answers. Often, you want to try to understand it yourself.' This highlights a clear preference for AI's efficiency in basic information retrieval, transforming the dynamic between students and educators.
40%
decrease in direct student-teacher interactions for generalizable tasks.
GenAI challenges the ethics of cheating—Defined by students' descriptions of how GenAI is used in ways they perceive as acceptable or unacceptable.
Case Study: Navigating the Ethics of AI Use
The introduction of GenAI has created tensions around academic integrity. While students are generally aware of the risks of plagiarism and value personal reflection, time constraints and workload pressures sometimes lead to deviations from these implicit rules. The study found that students might use AI to automate less 'meaningful' or urgent tasks, even if it borders on academic misconduct. Unclear or restrictive institutional rules further complicate this, sometimes driving students to find undetectable ways to use AI, creating a 'competition between students who want to exploit it in a bad way and the rules that try to stop it.' This highlights the need for adaptive policies that consider student workload and promote meaningful, AI-resistant assignments.
| Feature | Traditional Approach | AI-Enhanced Approach |
|---|---|---|
| Content Generation |
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| Problem Solving |
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| Academic Integrity |
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