A Systematic Mapping Study on Chatbots in Programming Education
- URL: http://arxiv.org/abs/2509.08857v1
- Date: Tue, 09 Sep 2025 15:25:01 GMT
- Title: A Systematic Mapping Study on Chatbots in Programming Education
- Abstract summary: This paper presents a Systematic Mapping Study (SMS) that investigated how such agents have been developed and applied in programming education.<n>From an initial set of 3,216 publications, 54 studies were selected and analyzed based on five research subquestions.
- Score: 0.3794345039149849
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Educational chatbots have gained prominence as support tools for teaching programming, particularly in introductory learning contexts. This paper presents a Systematic Mapping Study (SMS) that investigated how such agents have been developed and applied in programming education. From an initial set of 3,216 publications, 54 studies were selected and analyzed based on five research subquestions, addressing chatbot types, programming languages used, educational content covered, interaction models, and application contexts. The results reveal a predominance of chatbots designed for Python instruction, focusing on fundamental programming concepts, and employing a wide variety of pedagogical approaches and technological architectures. In addition to identifying trends and gaps in the literature, this study provides insights to inform the development of new educational tools for programming instruction.
Related papers
- Embodied Robot Manipulation in the Era of Foundation Models: Planning and Learning Perspectives [101.22313265304979]
Recent advances in vision, language, and multimodal learning have accelerated progress in robotic foundation models.<n>This survey examines robot manipulation from an algorithmic perspective.<n>We organize recent learning-based approaches within a unified abstraction of high-level planning and low-level control.
arXiv Detail & Related papers (2025-12-28T16:05:38Z) - From Textbook to Talkbot: A Case Study of a Greek-Language RAG-Based Chatbot in Higher Education [0.0]
The study aims to evaluate the effectiveness, reliability, and perceived usability of RAG based chatbots in higher education.<n>Findings from this research are expected to contribute to the emerging field of AI driven education.
arXiv Detail & Related papers (2025-12-23T22:40:03Z) - Teaching Introduction to Programming in the times of AI: A case study of a course re-design [0.9374652839580183]
The integration of AI tools into programming education has become increasingly prevalent in recent years.<n>This paper provides a review of the state-of-the-art AI tools available for teaching and learning programming, particularly in the context of introductory courses.<n>It highlights the challenges on course design, learning objectives, course delivery and formative and summative assessment, as well as the misuse of such tools by the students.
arXiv Detail & Related papers (2025-08-07T08:56:19Z) - Teaching Programming in the Age of Generative AI: Insights from Literature, Pedagogical Proposals, and Student Perspectives [0.0]
This article aims to review the most relevant studies on how programming content should be taught, learned, and assessed.<n>It proposes enriching teaching and learning methodologies by focusing on code comprehension and execution.<n>It advocates for the use of visual representations of code and visual simulations of its execution as effective tools for teaching, learning, and assessing programming.
arXiv Detail & Related papers (2025-06-30T17:38:27Z) - A systematic review of research on large language models for computer programming education [0.0]
Large language models (LLMs) play a critical role in computer programming education.<n>This study provides a systematic review of selected empirical studies on LLMs in computer programming education.
arXiv Detail & Related papers (2025-04-13T20:13:45Z) - DiSciPLE: Learning Interpretable Programs for Scientific Visual Discovery [61.02102713094486]
Good interpretation is important in scientific reasoning, as it allows for better decision-making.<n>This paper introduces an automatic way of obtaining such interpretable-by-design models, by learning programs that interleave neural networks.<n>We propose DiSciPLE an evolutionary algorithm that leverages common sense and prior knowledge of large language models (LLMs) to create Python programs explaining visual data.
arXiv Detail & Related papers (2025-02-14T10:26:14Z) - Enhancing Higher Education with Generative AI: A Multimodal Approach for Personalised Learning [2.334887570960192]
This research explores the opportunities of Generative AI (GenAI) in the realm of higher education.<n>We showcase the potential of GenAI in addressing a broad spectrum of educational queries.<n>By demonstrating a practical web application, this research underlines the imperative for integrating GenAI technologies to foster more dynamic and responsive educational environments.
arXiv Detail & Related papers (2025-02-11T09:29:29Z) - Tool Learning with Large Language Models: A Survey [60.733557487886635]
Tool learning with large language models (LLMs) has emerged as a promising paradigm for augmenting the capabilities of LLMs to tackle highly complex problems.
Despite growing attention and rapid advancements in this field, the existing literature remains fragmented and lacks systematic organization.
arXiv Detail & Related papers (2024-05-28T08:01:26Z) - From Pixels to Insights: A Survey on Automatic Chart Understanding in the Era of Large Foundation Models [98.41645229835493]
Data visualization in the form of charts plays a pivotal role in data analysis, offering critical insights and aiding in informed decision-making.<n>Large foundation models, such as large language models, have revolutionized various natural language processing tasks.<n>This survey paper serves as a comprehensive resource for researchers and practitioners in the fields of natural language processing, computer vision, and data analysis.
arXiv Detail & Related papers (2024-03-18T17:57:09Z) - Language Evolution with Deep Learning [49.879239655532324]
Computational modeling plays an essential role in the study of language emergence.
It aims to simulate the conditions and learning processes that could trigger the emergence of a structured language.
This chapter explores another class of computational models that have recently revolutionized the field of machine learning: deep learning models.
arXiv Detail & Related papers (2024-03-18T16:52:54Z) - AI Chatbots as Multi-Role Pedagogical Agents: Transforming Engagement in
CS Education [8.898863361318817]
We develop, implement, and evaluate a novel learning environment enriched with four distinct chatbots.
These roles cater to the three innate psychological needs of learners - competence, autonomy, and relatedness.
The system embraces an inquiry-based learning paradigm, encouraging students to ask questions, seek solutions, and explore their curiosities.
arXiv Detail & Related papers (2023-08-08T02:13:44Z) - A Survey of Deep Learning for Mathematical Reasoning [71.88150173381153]
We review the key tasks, datasets, and methods at the intersection of mathematical reasoning and deep learning over the past decade.
Recent advances in large-scale neural language models have opened up new benchmarks and opportunities to use deep learning for mathematical reasoning.
arXiv Detail & Related papers (2022-12-20T18:46:16Z) - Broader terms curriculum mapping: Using natural language processing and
visual-supported communication to create representative program planning
experiences [62.997667081978825]
Communication difficulties between faculty and non-faculty groups leave unexplored an immense collaboration potential.
This paper presents a method to deliver program plan representations that are universal, self-explanatory, and empowering.
arXiv Detail & Related papers (2021-02-09T13:27:04Z)
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.