Tool or Trouble? Exploring Student Attitudes Toward AI Coding Assistants
- URL: http://arxiv.org/abs/2507.22900v1
- Date: Thu, 26 Jun 2025 05:59:23 GMT
- Title: Tool or Trouble? Exploring Student Attitudes Toward AI Coding Assistants
- Authors: Sergio Rojas-Galeano,
- Abstract summary: Students completed a programming task with access to AI support; in the second, they extended their solutions without AI.<n>Findings suggest that AI tools were perceived as helpful for understanding code and increasing confidence, particularly during initial development.<n>Students reported difficulties transferring knowledge to unaided tasks, revealing possible overreliance and gaps in conceptual understanding.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: This exploratory study examines how AI code assistants shape novice programmers' experiences during a two-part exam in an introductory programming course. In the first part, students completed a programming task with access to AI support; in the second, they extended their solutions without AI. We collected Likert-scale and open-ended responses from 20 students to evaluate their perceptions and challenges. Findings suggest that AI tools were perceived as helpful for understanding code and increasing confidence, particularly during initial development. However, students reported difficulties transferring knowledge to unaided tasks, revealing possible overreliance and gaps in conceptual understanding. These insights highlight the need for pedagogical strategies that integrate AI meaningfully while reinforcing foundational programming skills.
Related papers
- Evaluating AI-Powered Learning Assistants in Engineering Higher Education: Student Engagement, Ethical Challenges, and Policy Implications [0.2812395851874055]
This study evaluates the use of the Educational AI Hub, an AI-powered learning framework, in undergraduate civil and environmental engineering courses at a large R1 public university.<n>Students appreciated the AI assistant for its convenience and comfort, with nearly half reporting greater ease in using the AI tool.<n>While most students viewed AI use as ethically acceptable, many expressed uncertainties about institutional policies and apprehension about potential academic misconduct.
arXiv Detail & Related papers (2025-06-06T03:02:49Z) - Sakshm AI: Advancing AI-Assisted Coding Education for Engineering Students in India Through Socratic Tutoring and Comprehensive Feedback [1.9841192743072902]
Existing AI tools for programming education struggle with key challenges, including the lack of Socratic guidance.<n>This study examines 1170 registered participants, analyzing platform logs, engagement trends, and problem-solving behavior to assess Sakshm AI's impact.
arXiv Detail & Related papers (2025-03-16T12:13:29Z) - Let people fail! Exploring the influence of explainable virtual and robotic agents in learning-by-doing tasks [45.23431596135002]
This study compares the effects of classic vs. partner-aware explanations on human behavior and performance during a learning-by-doing task.
Results indicated that partner-aware explanations influenced participants differently based on the type of artificial agents involved.
arXiv Detail & Related papers (2024-11-15T13:22:04Z) - A Multi-Year Grey Literature Review on AI-assisted Test Automation [46.97326049485643]
Test Automation (TA) techniques are crucial for quality assurance in software engineering but face limitations.<n>Given the prevalent usage of AI in industry, sources of truth are held in grey literature as well as the minds of professionals.<n>This study surveys grey literature to explore how AI is adopted in TA, focusing on the problems it solves, its solutions, and the available tools.
arXiv Detail & Related papers (2024-08-12T15:26:36Z) - Could ChatGPT get an Engineering Degree? Evaluating Higher Education Vulnerability to AI Assistants [176.39275404745098]
We evaluate whether two AI assistants, GPT-3.5 and GPT-4, can adequately answer assessment questions.<n>GPT-4 answers an average of 65.8% of questions correctly, and can even produce the correct answer across at least one prompting strategy for 85.1% of questions.<n>Our results call for revising program-level assessment design in higher education in light of advances in generative AI.
arXiv Detail & Related papers (2024-08-07T12:11:49Z) - Effects of a Prompt Engineering Intervention on Undergraduate Students' AI Self-Efficacy, AI Knowledge and Prompt Engineering Ability: A Mixed Methods Study [36.48421439947282]
This study designed and implemented a prompt engineering intervention at a university in Hong Kong.
It examined students' AI self-efficacy, AI knowledge, and proficiency in creating effective prompts.
arXiv Detail & Related papers (2024-07-30T15:05:24Z) - The Widening Gap: The Benefits and Harms of Generative AI for Novice Programmers [1.995977018536036]
Novice programmers often struggle through programming problem solving due to a lack of metacognitive awareness and strategies.
Many novices are now programming with generative AI (GenAI)
Our findings show an unfortunate divide in the use of GenAI tools between students who accelerated and students who struggled.
arXiv Detail & Related papers (2024-05-28T01:48:28Z) - Toward enriched Cognitive Learning with XAI [44.99833362998488]
We introduce an intelligent system (CL-XAI) for Cognitive Learning which is supported by artificial intelligence (AI) tools.
The use of CL-XAI is illustrated with a game-inspired virtual use case where learners tackle problems to enhance problem-solving skills.
arXiv Detail & Related papers (2023-12-19T16:13:47Z) - Developer Experiences with a Contextualized AI Coding Assistant:
Usability, Expectations, and Outcomes [11.520721038793285]
This study focuses on the initial experiences of 62 participants who used a contextualized coding AI assistant -- named StackSpot AI -- in a controlled setting.
Assistants' use resulted in significant time savings, easier access to documentation, and the generation of accurate codes for internal APIs.
challenges associated with the knowledge sources necessary to make the coding assistant access more contextual information as well as variable responses and limitations in handling complex codes were observed.
arXiv Detail & Related papers (2023-11-30T10:52:28Z) - Students' Perspective on AI Code Completion: Benefits and Challenges [2.936007114555107]
We investigated the benefits, challenges, and expectations of AI code completion from students' perspectives.
Our findings show that AI code completion enhanced students' productivity and efficiency by providing correct syntax suggestions.
In the future, AI code completion should be explainable and provide best coding practices to enhance the education process.
arXiv Detail & Related papers (2023-10-31T22:41:16Z) - Learning to Prompt in the Classroom to Understand AI Limits: A pilot
study [35.06607166918901]
Large Language Models (LLM) and the derived chatbots, like ChatGPT, have highly improved the natural language processing capabilities of AI systems.
However, excitement has led to negative sentiments, even as AI methods demonstrate remarkable contributions.
A pilot educational intervention was performed in a high school with 21 students.
arXiv Detail & Related papers (2023-07-04T07:51:37Z) - Seamful XAI: Operationalizing Seamful Design in Explainable AI [59.89011292395202]
Mistakes in AI systems are inevitable, arising from both technical limitations and sociotechnical gaps.
We propose that seamful design can foster AI explainability by revealing sociotechnical and infrastructural mismatches.
We explore this process with 43 AI practitioners and real end-users.
arXiv Detail & Related papers (2022-11-12T21:54:05Z) - HALMA: Humanlike Abstraction Learning Meets Affordance in Rapid Problem
Solving [104.79156980475686]
Humans learn compositional and causal abstraction, ie, knowledge, in response to the structure of naturalistic tasks.
We argue there shall be three levels of generalization in how an agent represents its knowledge: perceptual, conceptual, and algorithmic.
This benchmark is centered around a novel task domain, HALMA, for visual concept development and rapid problem-solving.
arXiv Detail & Related papers (2021-02-22T20:37:01Z)
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.