arXiv subjects

Juho Leinonen

Publications and source records attributed to Juho Leinonen.

At least 37 records · Page 2Linked to original sources

arXiv2026

Personalized Worked Example Generation from Student Code Submissions Using Pattern-based Knowledge Components

Adaptive programming practice often relies on fixed libraries of worked examples and practice problems, which require substantial authoring effort and may not correspond well to the logical errors and partial solutions students produce while writing code. As a result, students may receive learning content that does not directly address the concepts they are working to understand, while instructors must either invest additional effort in expanding content libraries or accept a coarse level of personalization. We present an approach for knowledge-component (KC) guided educational content generation using pattern-based KCs extracted from student code. Given a problem statement and student submissions, our pipeline extracts recurring structural KC patterns from students' code through AST-based analysis and uses them to condition a generative model. In this study, we apply this approach to worked example generation, and compare baseline and KC-conditioned outputs through expert evaluation. Results suggest that KC-conditioned generation improves topical focus and relevance to students' underlying logical errors, providing evidence that KC-based steering of generative models can support personalized learning at scale.

cs.HC↗

arXiv2026

Fast and Forgettable: A Controlled Study of Novices' Performance, Learning, Workload, and Emotion in AI-Assisted and Human Pair Programming Paradigms

Code-generating Artificial Intelligence has gained popularity within both professional and educational programming settings over the past several years. While research and pedagogy are beginning to cope with this change, computing students are left to bear the unforeseen consequences of AI amidst a dearth of empirical evidence about its effects. Though pair programming between students is well studied and known to be beneficial to self-efficacy and academic achievement, it remains underutilized and further threatened by the proposition that AI can replace a human programming partner. In this paper, we present a controlled pair programming study with 22 participants who wrote Python code under time pressure in teams of two and individually with GitHub Copilot for 20 minutes each. They were incentivized by bonus compensation to balance performance with understanding and were retested individually on the programming tasks after a retention interval of one week. Subjective measures of workload and emotion as well as objective measures of performance and learning (retest performance) were collected. Results showed that participants performed significantly better with GitHub Copilot than their human teammate, and several dimensions of their workload were significantly reduced. However, the emotional effect of the human teammate was significantly more positive and arousing as compared to working with Copilot. Furthermore, there was a nonsignificant absolute retest performance reduction in the AI condition and a larger retest performance decrement in the AI condition. We recommend that educators strongly consider revisiting pair programming as an educational tool in addition to embracing modern AI.

cs.HC↗

arXiv2025

Humanizing Automated Programming Feedback: Fine-Tuning Generative Models with Student-Written Feedback

The growing need for automated and personalized feedback in programming education has led to recent interest in leveraging generative AI for feedback generation. However, current approaches tend to rely on prompt engineering techniques in which predefined prompts guide the AI to generate feedback. This can result in rigid and constrained responses that fail to accommodate the diverse needs of students and do not reflect the style of human-written feedback from tutors or peers. In this study, we explore learnersourcing as a means to fine-tune language models for generating feedback that is more similar to that written by humans, particularly peer students. Specifically, we asked students to act in the flipped role of a tutor and write feedback on programs containing bugs. We collected approximately 1,900 instances of student-written feedback on multiple programming problems and buggy programs. To establish a baseline for comparison, we analyzed a sample of 300 instances based on correctness, length, and how the bugs are described. Using this data, we fine-tuned open-access generative models, specifically Llama3 and Phi3. Our findings indicate that fine-tuning models on learnersourced data not only produces feedback that better matches the style of feedback written by students, but also improves accuracy compared to feedback generated through prompt engineering alone, even though some student-written feedback is incorrect. This surprising finding highlights the potential of student-centered fine-tuning to improve automated feedback systems in programming education.

cs.CY↗

arXiv2025

Narrowing the Gap: Supervised Fine-Tuning of Open-Source LLMs as a Viable Alternative to Proprietary Models for Pedagogical Tools

Frontier Large language models (LLMs) like ChatGPT and Gemini can decipher cryptic compiler errors for novice programmers, but their computational scale, cost, and tendency to over-assist make them problematic for widespread pedagogical adoption. This work demonstrates that smaller, specialised language models, enhanced via Supervised Fine-Tuning (SFT), present a more viable alternative for educational tools. We utilise a new dataset of 40,000 C compiler error explanations, derived from real introductory programming (CS1/2) student-generated programming errors, which we used to fine-tune three open-source models: Qwen3-4B, Llama-3.1-8B, and Qwen3-32B. We performed a dual evaluation, combining expert human reviews with a large-scale automated analysis of 8,000 responses using a validated LLM-as-judge ensemble. Our results show that SFT significantly boosts the pedagogical quality of smaller models, achieving performance comparable to much larger models. We analyse the trade-offs between model size and quality, confirming that fine-tuning compact, efficient models on high-quality, domain-specific data is a potent strategy for creating specialised models to drive educational tools. We provide a replicable methodology to foster broader access to generative AI capabilities in educational contexts.

cs.CY↗

arXiv2025

SPIRAL integration of generative AI in an undergraduate creative media course: effects on self-efficacy and career outcome expectations

Computing education and computing students are rapidly integrating generative AI, but we know relatively little about how different pedagogical strategies for intentionally integrating generative AI affect students' self-efficacy and career interests. This study investigates a SPIRAL integration of generative AI (Skills Practiced Independently, Revisited with AI Later), implemented in an introductory undergraduate creative media and technology course in Fall 2023 (n=31). Students first developed domain skills for half the semester, then revisited earlier material integrating using generative AI, with explicit instruction on how to use it critically and ethically. We contribute a mixed methods quantitative and qualitative analysis of changes in self-efficacy and career interests over time, including longitudinal qualitative interviews (n=9) and thematic analysis. We found positive changes in both students' creative media self-efficacy and generative AI use self-efficacy, and mixed changes for ethical generative AI use self-efficacy. We also found students experienced demystification, transitioning from initial fear about generative AI taking over their fields and jobs, to doubting AI capability to do so and/or that society will push back against AI, through personal use of AI and observing others' use of AI vicariously. For career interests, our SPIRAL integration of generative AI use appeared to have either a neutral or positive influence on students, including widening their perceived career options, depending on their view of how AI would influence the career itself. These findings suggest that careful pedagogical sequencing can mitigate some potential negative impacts of AI, while promoting ethical and critical AI use that supports or has a neutral effect on students' career formation. To our knowledge our SPIRAL integration strategy applied to generative AI integration is novel.

cs.HC↗

arXiv2025

From Prompts to Propositions: A Logic-Based Lens on Student-LLM Interactions

Background and Context. The increasing integration of large language models (LLMs) in computing education presents an emerging challenge in understanding how students use LLMs and craft prompts to solve computational tasks. Prior research has used both qualitative and quantitative methods to analyze prompting behavior, but these approaches lack scalability or fail to effectively capture the semantic evolution of prompts. Objective. In this paper, we investigate whether students prompts can be systematically analyzed using propositional logic constraints. We examine whether this approach can identify patterns in prompt evolution, detect struggling students, and provide insights into effective and ineffective strategies. Method. We introduce Prompt2Constraints, a novel method that translates students prompts into logical constraints. The constraints are able to represent the intent of the prompts in succinct and quantifiable ways. We used this approach to analyze a dataset of 1,872 prompts from 203 students solving introductory programming tasks. Findings. We find that while successful and unsuccessful attempts tend to use a similar number of constraints overall, when students fail, they often modify their prompts more significantly, shifting problem-solving strategies midway. We also identify points where specific interventions could be most helpful to students for refining their prompts. Implications. This work offers a new and scalable way to detect students who struggle in solving natural language programming tasks. This work could be extended to investigate more complex tasks and integrated into programming tools to provide real-time support.

cs.HC↗

arXiv2025

Probing the Unknown: Exploring Student Interactions with Probeable Problems at Scale in Introductory Programming

Introductory programming courses often rely on small code-writing exercises that have clearly specified problem statements. This limits opportunities for students to practice how to clarify ambiguous requirements -- a critical skill in real-world programming. In addition, the emerging capabilities of large language models (LLMs) to produce code from well-defined specifications may harm student engagement with traditional programming exercises. This study explores the use of ``Probeable Problems'', automatically gradable tasks that have deliberately vague or incomplete specifications. Such problems require students to submit test inputs, or `probes', to clarify requirements before implementation. Through analysis of over 40,000 probes in an introductory course, we identify patterns linking probing behaviors to task success. Systematic strategies, such as thoroughly exploring expected behavior before coding, resulted in fewer incorrect code submissions and correlated with course success. Feedback from nearly 1,000 participants highlighted the challenges and real-world relevance of these tasks, as well as benefits to critical thinking and metacognitive skills. Probeable Problems are easy to set up and deploy at scale, and help students recognize and resolve uncertainties in programming problems.

cs.HC↗

arXiv2025

The Role of Generative AI in Software Student CollaborAItion

Collaboration is a crucial part of computing education. The increase in AI capabilities over the last couple of years is bound to profoundly affect all aspects of systems and software engineering, including collaboration. In this position paper, we consider a scenario where AI agents would be able to take on any role in collaborative processes in computing education. We outline these roles, the activities and group dynamics that software development currently include, and discuss if and in what way AI could facilitate these roles and activities. The goal of our work is to envision and critically examine potential futures. We present scenarios suggesting how AI can be integrated into existing collaborations. These are contrasted by design fictions that help demonstrate the new possibilities and challenges for computing education in the AI era.

cs.SE↗

arXiv2025

Exploring Iterative Enhancement for Improving Learnersourced Multiple-Choice Question Explanations with Large Language Models

Large language models exhibit superior capabilities in processing and understanding language, yet their applications in educational contexts remain underexplored. Learnersourcing enhances learning by engaging students in creating their own educational content. When learnersourcing multiple-choice questions, creating explanations for the solution of a question is a crucial step; it helps other students understand the solution and promotes a deeper understanding of related concepts. However, it is often difficult for students to craft effective solution explanations, due to limited subject understanding. To help scaffold the task of automated explanation generation, we present and evaluate a framework called "ILearner-LLM", that iteratively enhances the generated explanations for the given questions with large language models. Comprising an explanation generation model and an explanation evaluation model, the framework generates high-quality student-aligned explanations by iteratively feeding the quality rating score from the evaluation model back into the instruction prompt of the explanation generation model. Experimental results demonstrate the effectiveness of our ILearner-LLM on LLaMA2-13B and GPT-4 to generate higher quality explanations that are closer to those written by students on five PeerWise datasets. Our findings represent a promising path to enrich the learnersourcing experience for students and to enhance the capabilities of large language models for educational applications.

cs.AI↗

arXiv2024

Beyond the Hype: A Comprehensive Review of Current Trends in Generative AI Research, Teaching Practices, and Tools

Generative AI (GenAI) is advancing rapidly, and the literature in computing education is expanding almost as quickly. Initial responses to GenAI tools were mixed between panic and utopian optimism. Many were fast to point out the opportunities and challenges of GenAI. Researchers reported that these new tools are capable of solving most introductory programming tasks and are causing disruptions throughout the curriculum. These tools can write and explain code, enhance error messages, create resources for instructors, and even provide feedback and help for students like a traditional teaching assistant. In 2024, new research started to emerge on the effects of GenAI usage in the computing classroom. These new data involve the use of GenAI to support classroom instruction at scale and to teach students how to code with GenAI. In support of the former, a new class of tools is emerging that can provide personalized feedback to students on their programming assignments or teach both programming and prompting skills at the same time. With the literature expanding so rapidly, this report aims to summarize and explain what is happening on the ground in computing classrooms. We provide a systematic literature review; a survey of educators and industry professionals; and interviews with educators using GenAI in their courses, educators studying GenAI, and researchers who create GenAI tools to support computing education. The triangulation of these methods and data sources expands the understanding of GenAI usage and perceptions at this critical moment for our community.

cs.CY↗

arXiv2024

Breaking the Programming Language Barrier: Multilingual Prompting to Empower Non-Native English Learners

Non-native English speakers (NNES) face multiple barriers to learning programming. These barriers can be obvious, such as the fact that programming language syntax and instruction are often in English, or more subtle, such as being afraid to ask for help in a classroom full of native English speakers. However, these barriers are frustrating because many NNES students know more about programming than they can articulate in English. Advances in generative AI (GenAI) have the potential to break down these barriers because state of the art models can support interactions in multiple languages. Moreover, recent work has shown that GenAI can be highly accurate at code generation and explanation. In this paper, we provide the first exploration of NNES students prompting in their native languages (Arabic, Chinese, and Portuguese) to generate code to solve programming problems. Our results show that students are able to successfully use their native language to solve programming problems, but not without some difficulty specifying programming terminology and concepts. We discuss the challenges they faced, the implications for practice in the short term, and how this might transform computing education globally in the long term.

cs.CY↗

arXiv2024

Howzat? Appealing to Expert Judgement for Evaluating Human and AI Next-Step Hints for Novice Programmers

Motivation: Students learning to program often reach states where they are stuck and can make no forward progress. An automatically generated next-step hint can help them make forward progress and support their learning. It is important to know what makes a good hint or a bad hint, and how to generate good hints automatically in novice programming tools, for example using Large Language Models (LLMs). Method and participants: We recruited 44 Java educators from around the world to participate in an online study. We used a set of real student code states as hint-generation scenarios. Participants used a technique known as comparative judgement to rank a set of candidate next-step Java hints, which were generated by Large Language Models (LLMs) and by five human experienced educators. Participants ranked the hints without being told how they were generated. Findings: We found that LLMs had considerable variation in generating high quality next-step hints for programming novices, with GPT-4 outperforming other models tested. When used with a well-designed prompt, GPT-4 outperformed human experts in generating pedagogically valuable hints. A multi-stage prompt was the most effective LLM prompt. We found that the two most important factors of a good hint were length (80--160 words being best), and reading level (US grade 9 or below being best). Offering alternative approaches to solving the problem was considered bad, and we found no effect of sentiment. Conclusions: Automatic generation of these hints is immediately viable, given that LLMs outperformed humans -- even when the students' task is unknown. The fact that only the best prompts achieve this outcome suggests that students on their own are unlikely to be able to produce the same benefit. The prompting task, therefore, should be embedded in an expert-designed tool.

cs.CY↗

arXiv2024

Evaluating Language Models for Generating and Judging Programming Feedback

The emergence of large language models (LLMs) has transformed research and practice across a wide range of domains. Within the computing education research (CER) domain, LLMs have garnered significant attention, particularly in the context of learning programming. Much of the work on LLMs in CER, however, has focused on applying and evaluating proprietary models. In this article, we evaluate the efficiency of open-source LLMs in generating high-quality feedback for programming assignments and judging the quality of programming feedback, contrasting the results with proprietary models. Our evaluations on a dataset of students' submissions to introductory Python programming exercises suggest that state-of-the-art open-source LLMs are nearly on par with proprietary models in both generating and assessing programming feedback. Additionally, we demonstrate the efficiency of smaller LLMs in these tasks and highlight the wide range of LLMs accessible, even for free, to educators and practitioners.

cs.AI↗

arXiv2024

On the Opportunities of Large Language Models for Programming Process Data

Computing educators and researchers have used programming process data to understand how programs are constructed and what sorts of problems students struggle with. Although such data shows promise for using it for feedback, fully automated programming process feedback systems have still been an under-explored area. The recent emergence of large language models (LLMs) have yielded additional opportunities for researchers in a wide variety of fields. LLMs are efficient at transforming content from one format to another, leveraging the body of knowledge they have been trained with in the process. In this article, we discuss opportunities of using LLMs for analyzing programming process data. To complement our discussion, we outline a case study where we have leveraged LLMs for automatically summarizing the programming process and for creating formative feedback on the programming process. Overall, our discussion and findings highlight that the computing education research and practice community is again one step closer to automating formative programming process-focused feedback.

cs.CY↗

arXiv2024

LLM-itation is the Sincerest Form of Data: Generating Synthetic Buggy Code Submissions for Computing Education

There is a great need for data in computing education research. Data is needed to understand how students behave, to train models of student behavior to optimally support students, and to develop and validate new assessment tools and learning analytics techniques. However, relatively few computing education datasets are shared openly, often due to privacy regulations and issues in making sure the data is anonymous. Large language models (LLMs) offer a promising approach to create large-scale, privacy-preserving synthetic data, which can be used to explore various aspects of student learning, develop and test educational technologies, and support research in areas where collecting real student data may be challenging or impractical. This work explores generating synthetic buggy code submissions for introductory programming exercises using GPT-4o. We compare the distribution of test case failures between synthetic and real student data from two courses to analyze the accuracy of the synthetic data in mimicking real student data. Our findings suggest that LLMs can be used to generate synthetic incorrect submissions that are not significantly different from real student data with regard to test case failure distributions. Our research contributes to the development of reliable synthetic datasets for computing education research and teaching, potentially accelerating progress in the field while preserving student privacy.

cs.CY↗

arXiv2024

Synthetic Students: A Comparative Study of Bug Distribution Between Large Language Models and Computing Students

Large language models (LLMs) present an exciting opportunity for generating synthetic classroom data. Such data could include code containing a typical distribution of errors, simulated student behaviour to address the cold start problem when developing education tools, and synthetic user data when access to authentic data is restricted due to privacy reasons. In this research paper, we conduct a comparative study examining the distribution of bugs generated by LLMs in contrast to those produced by computing students. Leveraging data from two previous large-scale analyses of student-generated bugs, we investigate whether LLMs can be coaxed to exhibit bug patterns that are similar to authentic student bugs when prompted to inject errors into code. The results suggest that unguided, LLMs do not generate plausible error distributions, and many of the generated errors are unlikely to be generated by real students. However, with guidance including descriptions of common errors and typical frequencies, LLMs can be shepherded to generate realistic distributions of errors in synthetic code.

cs.CY↗

arXiv2024

"Sometimes You Just Gotta Risk It for the Biscuit": A Portrait of Student Risk-Taking

Understanding how individuals make decisions involving risk is a fundamental aspect of behavioral research. Despite the ubiquity of risk in various aspects of life, limited empirical work has explored student risk-taking behavior in computing education. This study aims to partially replicate prior research on risk-taking behavior in software engineers while focusing on students, shedding light on the factors that affect their risk-taking choices. In our work, students were presented with a hypothetical scenario related to meeting a course project deadline, where they had to choose between a risky option and a safer alternative. We examined several factors that might influence these choices, including the framing of the decision (as a potential gain or loss), students' enjoyment of programming, perceived difficulty of programming, and their performance in the course. Our findings reveal intriguing insights into student risk-taking behavior. Similar to software engineers in prior work, the framing of the decision significantly impacted the choices students made, with the loss framing leading to a higher likelihood for risky choices. Surprisingly, students displayed a greater inclination towards risk-taking compared to their professional counterparts in prior research. Furthermore, we observed that students' performance in the course and their enjoyment of programming had a subtle correlation with their risk-taking tendencies, with better-performing students and those who enjoyed programming being marginally more prone to taking risks. Notably, we did not find statistically significant correlations between perceived difficulty of programming and risk-taking behavior among students.

cs.SE↗

arXiv2024

Integrating Natural Language Prompting Tasks in Introductory Programming Courses

Introductory programming courses often emphasize mastering syntax and basic constructs before progressing to more complex and interesting programs. This bottom-up approach can be frustrating for novices, shifting the focus away from problem solving and potentially making computing less appealing to a broad range of students. The rise of generative AI for code production could partially address these issues by fostering new skills via interaction with AI models, including constructing high-level prompts and evaluating code that is automatically generated. In this experience report, we explore the inclusion of two prompt-focused activities in an introductory course, implemented across four labs in a six-week module. The first requires students to solve computational problems by writing natural language prompts, emphasizing problem-solving over syntax. The second involves students crafting prompts to generate code equivalent to provided fragments, to foster an understanding of the relationship between prompts and code. Most of the students in the course had reported finding programming difficult to learn, often citing frustrations with syntax and debugging. We found that self-reported difficulty with learning programming had a strong inverse relationship with performance on traditional programming assessments such as tests and projects, as expected. However, performance on the natural language tasks was less strongly related to self-reported difficulty, suggesting they may target different skills. Learning how to communicate with AI coding models is becoming an important skill, and natural language prompting tasks may appeal to a broad range of students.

cs.CY↗