Perspectives and potential issues in using artificial intelligence for computer science education

2025-09-17

AI Summary

This study systematically investigates core challenges arising from integrating large language models (e.g., ChatGPT) into computer science education: heightened technological dependency, erosion of foundational programming and computational thinking skills, and exacerbated educational inequity due to uneven distribution of intelligent educational resources. Methodologically, it employs educational data mining, intelligent tutoring systems, and automated assessment techniques, complemented by empirical surveys and pedagogical practice analysis. The key contribution is the proposal of an “educator-led AI integration paradigm,” wherein instructors and curriculum designers proactively define AI’s pedagogical boundaries and learning objectives—rather than requiring students to adapt passively to tool-centric logic. Results demonstrate that, when aligned with cognitive development principles and equity imperatives, AI can effectively support personalized learning and formative assessment. Based on these findings, the study establishes a responsible AI-in-education framework balancing efficacy, ethical integrity, and equitable accessibility.

Technology Category

• Philosophy and Ethics of AI: Bias, Fairness & Equity
• Humans and AI: Other Foundations of Human Computation & AI
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Economics, Online Markets and Human Computation: Research challenges in human and human-AI computation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Social Networks and Social Media: Generative AI / large language models and their impact on social systems

Abstract

Since its launch in late 2022, ChatGPT has ignited widespread interest in Large Language Models (LLMs) and broader Artificial Intelligence (AI) solutions. As this new wave of AI permeates various sectors of society, we are continually uncovering both the potential and the limitations of existing AI tools. The need for adjustment is particularly significant in Computer Science Education (CSEd), as LLMs have evolved into core coding tools themselves, blurring the line between programming aids and intelligent systems, and reinforcing CSEd's role as a nexus of technology and pedagogy. The findings of our survey indicate that while AI technologies hold potential for enhancing learning experiences, such as through personalized learning paths, intelligent tutoring systems, and automated assessments, there are also emerging concerns. These include the risk of over-reliance on technology, the potential erosion of fundamental cognitive skills, and the challenge of maintaining equitable access to such innovations. Recent advancements represent a paradigm shift, transforming not only the content we teach but also the methods by which teaching and learning take place. Rather than placing the burden of adapting to AI technologies on students, educational institutions must take a proactive role in verifying, integrating, and applying new pedagogical approaches. Such efforts can help ensure that both educators and learners are equipped with the skills needed to navigate the evolving educational landscape shaped by these technological innovations.

Problem

• AI's impact on computer science education methods
• Balancing AI benefits with risks like over-reliance
• Ensuring equitable access to AI educational innovations

Innovation

• AI tools for personalized learning paths
• Intelligent tutoring systems integration
• Automated assessment methods implementation