Integrating LLMs in Software Engineering Education: Motivators, Demotivators, and a Roadmap Towards a Framework for Finnish Higher Education Institutes
- URL: http://arxiv.org/abs/2503.22238v1
- Date: Fri, 28 Mar 2025 08:41:43 GMT
- Title: Integrating LLMs in Software Engineering Education: Motivators, Demotivators, and a Roadmap Towards a Framework for Finnish Higher Education Institutes
- Authors: Maryam Khan, Muhammad Azeem Akbar, Jussi Kasurinen,
- Abstract summary: The increasing adoption of Large Language Models (LLMs) in software engineering education presents both opportunities and challenges.<n>LLMs offer benefits such as enhanced learning experiences, automated assessments, and personalized tutoring.<n>Their integration also raises concerns about academic integrity, student over-reliance, and ethical considerations.
- Score: 1.2825802100149328
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The increasing adoption of Large Language Models (LLMs) in software engineering education presents both opportunities and challenges. While LLMs offer benefits such as enhanced learning experiences, automated assessments, and personalized tutoring, their integration also raises concerns about academic integrity, student over-reliance, and ethical considerations. In this study, we conducted a preliminary literature review to identify motivators and demotivators for using LLMs in software engineering education. We applied a thematic mapping process to categorize and structure these factors (motivators and demotivators), offering a comprehensive view of their impact. In total, we identified 25 motivators and 30 demotivators, which are further organized into four high-level themes. This mapping provides a structured framework for understanding the factors that influence the integration of LLMs in software engineering education, both positively and negatively. As part of a larger research project, this study serves as a feasibility assessment, laying the groundwork for future systematic literature review and empirical studies. Ultimately, this project aims to develop a framework to assist Finnish higher education institutions in effectively integrating LLMs into software engineering education while addressing potential risks and challenges.
Related papers
- Enhanced Bloom's Educational Taxonomy for Fostering Information Literacy in the Era of Large Language Models [16.31527042425208]
This paper proposes an LLM-driven Bloom's Educational Taxonomy that aims to recognize and evaluate students' information literacy (IL) with Large Language Models (LLMs)<n>The framework delineates the IL corresponding to the cognitive abilities required to use LLM into two distinct stages: Exploration & Action and Creation & Metacognition.
arXiv Detail & Related papers (2025-03-25T08:23:49Z) - LLM Agents for Education: Advances and Applications [49.3663528354802]
Large Language Model (LLM) agents have demonstrated remarkable capabilities in automating tasks and driving innovation across diverse educational applications.<n>This survey aims to provide a comprehensive technological overview of LLM agents for education, fostering further research and collaboration to enhance their impact for the greater good of learners and educators alike.
arXiv Detail & Related papers (2025-03-14T11:53:44Z) - Assessing LLMs for Front-end Software Architecture Knowledge [0.0]
Large Language Models (LLMs) have demonstrated significant promise in automating software development tasks.<n>This study investigates the capabilities of an LLM in understanding, reproducing, and generating structures within the VIPER architecture.<n> Experimental results, using ChatGPT 4 Turbo 2024-04-09, reveal that the LLM excelled in higher-order tasks like evaluating and creating, but faced challenges with lower-order tasks requiring precise retrieval of architectural details.
arXiv Detail & Related papers (2025-02-26T19:33:35Z) - An Overview of Large Language Models for Statisticians [109.38601458831545]
Large Language Models (LLMs) have emerged as transformative tools in artificial intelligence (AI)<n>This paper explores potential areas where statisticians can make important contributions to the development of LLMs.<n>We focus on issues such as uncertainty quantification, interpretability, fairness, privacy, watermarking and model adaptation.
arXiv Detail & Related papers (2025-02-25T03:40:36Z) - Position: LLMs Can be Good Tutors in Foreign Language Education [87.88557755407815]
We argue that large language models (LLMs) have the potential to serve as effective tutors in foreign language education (FLE)<n> Specifically, LLMs can play three critical roles: (1) as data enhancers, improving the creation of learning materials or serving as student simulations; (2) as task predictors, serving as learner assessment or optimizing learning pathway; and (3) as agents, enabling personalized and inclusive education.
arXiv Detail & Related papers (2025-02-08T06:48:49Z) - A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law [65.87885628115946]
Large language models (LLMs) are revolutionizing the landscapes of finance, healthcare, and law.
We highlight the instrumental role of LLMs in enhancing diagnostic and treatment methodologies in healthcare, innovating financial analytics, and refining legal interpretation and compliance strategies.
We critically examine the ethics for LLM applications in these fields, pointing out the existing ethical concerns and the need for transparent, fair, and robust AI systems.
arXiv Detail & Related papers (2024-05-02T22:43:02Z) - Rethinking Machine Unlearning for Large Language Models [85.92660644100582]
We explore machine unlearning in the domain of large language models (LLMs)
This initiative aims to eliminate undesirable data influence (e.g., sensitive or illegal information) and the associated model capabilities.
arXiv Detail & Related papers (2024-02-13T20:51:58Z) - An Empirical Study on Usage and Perceptions of LLMs in a Software
Engineering Project [1.433758865948252]
Large Language Models (LLMs) represent a leap in artificial intelligence, excelling in tasks using human language(s)
In this paper, we analyze the AI-generated code, prompts used for code generation, and the human intervention levels to integrate the code into the code base.
Our findings suggest that LLMs can play a crucial role in the early stages of software development.
arXiv Detail & Related papers (2024-01-29T14:32:32Z) - Towards an Understanding of Large Language Models in Software Engineering Tasks [29.30433406449331]
Large Language Models (LLMs) have drawn widespread attention and research due to their astounding performance in text generation and reasoning tasks.
The evaluation and optimization of LLMs in software engineering tasks, such as code generation, have become a research focus.
This paper comprehensively investigate and collate the research and products combining LLMs with software engineering.
arXiv Detail & Related papers (2023-08-22T12:37:29Z) - A Survey on Evaluation of Large Language Models [87.60417393701331]
Large language models (LLMs) are gaining increasing popularity in both academia and industry.
This paper focuses on three key dimensions: what to evaluate, where to evaluate, and how to evaluate.
arXiv Detail & Related papers (2023-07-06T16:28:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.