arXiv subjects

Paul Denny

Publications and source records attributed to Paul Denny.

At least 19 recordsLinked to original sources

arXiv2026

Mapping the Emerging Curriculum for AI-Assisted Software Engineering via Syllabus Analysis

As Generative AI coding tools reshape professional software development, universities have begun designing courses to prepare students for AI-assisted development workflows. By analyzing the syllabi of these courses, we can gather empirical evidence about these courses, reveal how this emerging curricular area is being defined, and gain guidance for future curriculum design. We analyzed 23 publicly available syllabi and course materials of upper-division, credit-bearing courses that meet specific criteria, including explicitly addressing Generative AI in software engineering. Through iterative qualitative coding, we characterized courses' learning objectives, assessments, topics, and documented AI tools. Our analysis reveals commonalities and differences among these courses that allow researchers and educators to study and develop future courses.

cs.SE↗

arXiv2026

Commenting with Copilot: A Taxonomy and Multi-Year Analysis of Student Code-Generation Specifications

As AI code tools become integrated into programming environments, students increasingly describe intended behavior in natural language and rely on these tools to generate code, shifting emphasis from code writing to specification. Yet little is known about the comments students write as specifications in AI-assisted programming tasks. We analyze a four-year dataset of undergraduate programming submissions and reflections from tasks in which students wrote comments to guide code generation and refined solutions using test-case feedback. We introduce a taxonomy spanning three dimensions: comment type, code expression level, and code construct. Using automated classification, we examine how these dimensions vary across attempts and how students describe the process in their reflections. Our findings show that students mostly wrote natural-language What comments, shifted toward How comments for more procedural constructs, and focused more on verifying generated code than on repeatedly rewriting comments.

cs.SE↗

arXiv2026

Say What? Examining Text and Voice Input Modalities for Prompt-Based Programming in Computing Education

Large language models (LLMs) are increasingly integrated into computing education, yet nearly all prior research has focused on text-based interactions. As voice-enabled interfaces become more capable and more common, there is growing interest in understanding how voice input might shape students' use of LLM-powered tools. In this exploratory study, we investigated how introductory programming students interact with Prompt Problems, which are programming tasks that require crafting natural-language prompts to generate correct code. Students (N = 919) solved a series of Prompt Problems with the freedom to select or switch between text and voice input modalities. We collected their prompt submissions as well as post-activity survey responses, then analysed differences in prompt accuracy, persistence, and perspectives by modality. For two of the three problems, we found that students who typed their prompts using text were more likely to have those prompts succeed on the first attempt than students who submitted unedited voice prompts. There was no difference in success rate if students edited their transcribed voice prompts before submission. Across the problems, we found evidence that students who tried voice prompting varied in their usage of modality - perhaps indicating a complementary, or non-preferential approach. However, most students only tried and reported preferring text. Our qualitative analysis revealed how students' perceived the roles of voice and text input in shaping their problem-solving process, as well as the reported drawbacks and advantages of each modality. We discuss implications for future multimodal tools and instructional design in computing education.

cs.CY↗

arXiv2026

Understanding Student Perceptions, Mistakes, and Debugging Approaches when Solving Natural Language Programming Tasks

Learning to communicate with code-generating AI models is an emerging skill for novice programmers. One recent pedagogical approach, Prompt Problems, has students solve computational tasks by writing natural-language prompts for code-generating AI models. However, little is known about the specific prompt-level mistakes novice programmers make, the kinds of computational details they fail to communicate, and what strategies they use to recover when generated code is incorrect. In a CS1 course, we studied attempts by more than 900 students to solve dialogue-based Prompt Problems. We analyzed student reflections, unsuccessful prompts, and reported debugging strategies. Compared to traditional coding tasks, students generally found prompting easier, more enjoyable, and better targeted at developing problem-solving skills. The most common mistakes are related to the omission of key details, suggesting both a failure to acknowledge their importance and over-reliance on AI to infer them. When prompts failed, students focused more on clarifying their intent and reflecting on the provided problem details than on tracing generated code or examining test cases.

cs.CY↗

arXiv2026

When AI Is Wrong on Purpose: How Students Respond to Buggy GenAI Code

As Generative AI (GenAI) becomes increasingly central to software development, CS education is integrating prompt-centered workflows where students describe intended program behavior in natural language to elicit code. However, professional practice requires careful review and verification of GenAI-generated code that may appear correct while containing subtle faults. This creates a challenge for CS1-level activities, where current models often solve tasks correctly and reduce students' incentive to closely inspect generated outputs. We investigate how prompt-centered programming activities can be adapted to better foster these practices. Specifically, we explore an approach where realistic, runnable bugs are injected into otherwise correct solutions, thus requiring students to read and repair generated outputs. We analyzed 2,636 sessions from 917 students, and examined behavior across instances of naturally occurring prompt-related failures and deliberately injected bugs within each session. Our findings show that students responded differently across bug sources. Deliberately injected bugs more often led to direct code edits and higher next-attempt success, suggesting localized repair of near-miss solutions. Prompt-related failures instead more often led students to refine prompts by clarifying constraints, updating function signatures, adding edge cases, or reframing the task. Student reflections reinforce the emphasis on review and repair, describing useful practice in code understanding, code review, and debugging, as well as a more careful verification mindset and greater awareness of GenAI limitations. Ultimately, prompt-related failures and injected bugs together support a pedagogically useful GenAI workflow, where students practice both specification refinement through prompts and debugging through code editing.

cs.SE↗

arXiv2026

Exploring the Value of Diverse LLM Explanations in Introductory Programming

Large Language Models (LLMs) have shown the potential to generate code explanations that surpass those of peers in quality, offering promising opportunities for computer science education. While these explanations may not yet match the depth and clarity of instructor-provided explanations, research in computational creativity highlights that the quantity and diversity of ideas can often outweigh a singular focus on quality. Inspired by this, we explore whether combining multiple diverse explanations, each emphasizing distinct aspects (e.g., function, concept, goal), can enhance students' understanding of programming exercises compared to generic explanations that do not emphasize distinct conceptual aspects. In our study 971 first-year computing students were randomly assigned either diverse or generic LLM-generated explanations for two programming exercises. Students completed multiple-choice and open-ended questions for each exercise, followed by Likert-scale questions and open-ended reflections. Our findings outline patterns in student performance and perceived cognitive load across the two explanation conditions. These findings highlight how variation in explanation emphasis may relate to learner engagement and understanding. Across participants, open-ended response accuracy was consistently about 7.7% higher when students received diverse explanations, with no difference in perceived cognitive load.

cs.HC↗

arXiv2026

Distinguishing Artificial from Authentic: Evaluating LLMs for Detecting LLM-Generated Content

As large language models (LLMs) are increasingly used by students to generate natural language responses and program code, there is growing interest in whether LLMs themselves can be used to distinguish AI-generated work from human-authored submissions. In this paper, we investigate the extent to which LLMs can detect their own generated content across multiple educational task types, including programming exercises, reflective writing, and short-answer questions. Using authentic student responses and multiple variants of LLM-generated answers, we evaluate detection performance under different prompting strategies and output formats. Our study addresses three research questions: (1) how accurately LLMs can identify their own outputs across task domains, (2) how detection effectiveness is influenced by factors such as prompt design, response length, and task type, and (3) what characteristics of LLM-generated responses contribute to successful or failed detection. Our findings show that LLM-based detection is highly task-dependent: detection is substantially more reliable for programming tasks and longer reflective responses, but performs poorly for short-answer questions, where LLMs frequently judge their own outputs as more human-like than authentic student responses. We further find that prompt framing and response verbosity have a pronounced effect on detectability in reflective writing tasks, with relatively minor prompt variations significantly reducing detection accuracy, while programming-related detection is more robust to prompt changes. Together, these results highlight both the potential and the limitations of LLM self-detection in educational settings and suggest caution in relying on LLMs as standalone tools for identifying AI-generated student work.

cs.CL↗

arXiv2026

RLearner-LLM: Balancing Logical Grounding and Fluency in Large Language Models via Hybrid Direct Preference Optimization

Direct Preference Optimization (DPO), the efficient alternative to PPO-based RLHF, falls short on knowledge-intensive generation: standard preference signals from human annotators or LLM judges exhibit a systematic verbosity bias that rewards fluency over logical correctness. This blindspot leaves a logical alignment gap -- SFT models reach NLI entailment of only 0.05-0.22 despite producing fluent text. We propose RLearner-LLM with Hybrid-DPO: an automated preference pipeline that fuses a DeBERTa-v3 NLI signal with a verifier LLM score, removing human annotation while overcoming the "alignment tax" of single-signal optimization. Evaluated across five academic domains (Biology, Medicine, Law) with three base architectures (LLaMA-2-13B, Qwen3-8B, Gemma 4 E4B-it), RLearner-LLM yields up to 6x NLI improvement over SFT, with NLI gains in 11 of 15 cells and consistent answer-coverage gains. On Gemma 4 E4B-it (4.5B effective params), Hybrid-DPO lifts NLI in four of five domains (+11.9% to +2.4x) with faster inference across all five, scaling down to compact base models without losing the alignment-tax mitigation. Our Qwen3-8B RLearner-LLM wins 95% of pairwise comparisons against its own SFT baseline; GPT-4o-mini in turn wins 95% against our concise output -- alongside the 69% win the same judge gives a verbose SFT over our DPO model, this replicates verbosity bias on a frontier comparator and motivates logic-aware metrics (NLI, ACR) over LLM-as-a-judge for knowledge-intensive generation.

cs.CL↗

arXiv2026

Building AI Companions that Prioritise Learning over Performance

Large language models (LLMs) are rapidly transforming knowledge work by improving the quality and efficiency of tasks such as writing, coding, and data analysis. However, their growing use in education has exposed a learning-performance paradox: while they can enhance short-term task performance, they may also undermine genuine learning, including cognitive growth, knowledge transfer, and metacognitive development. This paper addresses the question of how artificial intelligence should be designed and used to support learning rather than merely improve immediate outputs. We introduce the concept of AI learning companions, defined as adaptive, pedagogically informed, LLM-powered agents designed for integration into learning environments. We propose a framework for their design built on three interrelated foundations: a pedagogical foundation focused on how students learn with AI, an adaptive foundation focused on how AI learns about students, and a responsible design foundation ensuring systems remain transparent, accountable, inclusive, and secure. The framework is illustrated through five case studies spanning diverse educational contexts, levels, and tool designs, revealing both the promise and current limitations of existing tools. We conclude that there is a necessary shift away from LLMs designed for task-oriented performance, and beyond simply prompting them to act as tutors, toward deliberately developed AI learning companions that are pedagogically sound, adapt to their learners, and foster durable understanding, metacognitive growth, and learner agency.

cs.HC↗

arXiv2026

Reshaping Undergraduate Computer Science Education in the Generative AI Era

Generative AI represents a turning point for Computer Science (CS) education. In recent decades, post-secondary CS education has largely focused on what has been seen as practical software engineering skills: implementation-level programming, debugging, testing, and software design, analysis, and documentation. However, this framing is becoming less tenable as generative AI automates many of these tasks, challenging their centrality in CS education. To keep pace with advances in AI technology, CS curricula should consider a shift toward understanding and verifying AI-generated artifacts. This white paper outlines the findings of two international NUS-Google Workshops in Singapore, where we convened faculty members, industry practitioners, and students, and proposes a strategic response to reshape how CS should be taught at the undergraduate level. Based on the findings, we identify critical skills that must be preserved and those that are becoming less important. By incorporating these skills as "breadcrumbs," we can provide helpful nudges and engaging exercises within the current curriculum, enhancing learning experiences for everyone. We believe that to effectively prepare future computer science graduates, capable of creating, solving problems, and managing, as well as co-creating, artifacts with AI. It is important to consider a shift in curricula. Emphasizing system design, abstraction, and critical evaluation could greatly enhance their education and readiness for the challenges ahead. We propose prerequisites for solutions to reform CS education by fostering AI-native competencies, re-centering fundamental education, enhancing advanced pathways, embracing new pedagogies, and shifting institutional support.

cs.CY↗

arXiv2026

Designing and Evaluating Next-Generation Learning Interfaces: Linking AI, HCI, and the Learning Sciences

This workshop addresses this gap by bringing together researchers and practitioners from AI, HCI, and the learning sciences to explore how interactive systems can better support learning. We focus on the design and evaluation of human-AI collaborative learning interfaces that are technically robust, human-centered, and pedagogically grounded. By fostering interdisciplinary dialogue, the workshop aims to identify shared challenges, design principles, and research directions for next-generation learning technologies.

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↗

arXiv2026

Playsemble: Learning Low-Level Programming Through Interactive Games

Teaching assembly programming is a fundamental component of undergraduate computer science education, yet many students struggle with its abstract and low-level concepts. Existing learning tools, such as simulators and visualisers, support understanding by exposing machine states. However, they often limit students to passive observation and provide few opportunities for meaningful interaction. To address these limitations, we introduce Playsemble, a gamified learning system that transforms assembly instructions into interactive, game-like tasks in which students control Pac-Man to collect items, avoid ghosts, and reach targets. Playsemble integrates a code editor, a CPU emulator, and visual debugging tools within a browser-based environment, allowing students to work offline without installation or configuration. It also provides immediate formative feedback enhanced by large language models. We deployed Playsemble in an undergraduate computer architecture course with 107 students. The course featured a sequence of assignments of increasing complexity, covering core concepts such as register and memory manipulation, control structures including loops and conditionals, and arithmetic operations. Our findings suggest that Playsemble promotes active experimentation, sustained engagement, and deeper conceptual understanding through meaningful game-based learning experiences.

cs.CY↗

arXiv2025

Systematically Thinking about the Complexity of Code Structuring Exercises at Introductory Level

Decomposition and abstraction is an essential component of computational thinking, yet it is not always emphasized in introductory programming courses. In addition, as generative AI further reduces the focus on syntax and increases the importance of higher-level code reasoning, there is renewed opportunity to teach DA explicitly. In this paper, we introduce a framework for systematically assessing the complexity of code structuring tasks, where students must identify and separate meaningful abstractions within existing, unstructured code. The framework defines three dimensions of task complexity, each with multiple levels: repetition, code pattern, and data dependency. To support practical use, we provide example tasks mapped to these levels and offer an interactive tool for generating and exploring DA problems. The framework is designed to support the development of educational tasks that build students' skills with DA in the procedural paradigm.

cs.SE↗

arXiv2025

Data-Efficient Adaptation and a Novel Evaluation Method for Aspect-based Sentiment Analysis

Aspect-based Sentiment Analysis (ABSA) is a fine-grained opinion mining approach that identifies and classifies opinions associated with specific entities (aspects) or their categories within a sentence. Despite its rapid growth and broad potential, ABSA research and resources remain concentrated in commercial domains, leaving analytical needs unmet in high-demand yet low-resource areas such as education and healthcare. Domain adaptation challenges and most existing methods' reliance on resource-intensive in-training knowledge injection further hinder progress in these areas. Moreover, traditional evaluation methods based on exact matches are overly rigid for ABSA tasks, penalising any boundary variations which may misrepresent the performance of generative models. This work addresses these gaps through three contributions: 1) We propose a novel evaluation method, Flexible Text Similarity Matching and Optimal Bipartite Pairing (FTS-OBP), which accommodates realistic extraction boundary variations while maintaining strong correlation with traditional metrics and offering fine-grained diagnostics. 2) We present the first ABSA study of small decoder-only generative language models (SLMs; <7B parameters), examining resource lower bounds via a case study in education review ABSA. We systematically explore data-free (in-context learning and weight merging) and data-light fine-tuning methods, and propose a multitask fine-tuning strategy that significantly enhances SLM performance, enabling 1.5-3.8 B models to surpass proprietary large models and approach benchmark results with only 200-1,000 examples on a single GPU. 3) We release the first public set of education review ABSA resources to support future research in low-resource domains.

cs.CL↗

arXiv2025

Interleaving Natural Language Prompting with Code Editing for Solving Programming Tasks with Generative AI Models

Modern computing students often rely on both natural-language prompting and manual code editing to solve programming tasks. Yet we still lack a clear understanding of how these two modes are combined in practice, and how their usage varies with task complexity and student ability. In this paper, we investigate this through a large-scale study in an introductory programming course, collecting 13,305 interactions from 355 students during a three-day lab activity. Our analysis shows that students primarily use prompting to generate initial solutions, and then often enter short edit-run loops to refine their code following a failed execution. Student reflections confirm that prompting is helpful for structuring solutions, editing is effective for making targeted corrections, while both are useful for learning. We find that manual editing becomes more frequent as task complexity increases, but most edits remain concise, with many affecting a single line of code. Higher-performing students succeed with less reliance on editing and fewer overall interactions. These findings highlight the role of manual editing as a form of last-mile repair, complementing prompting in AI-assisted programming workflows.

cs.CY↗

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

EduRABSA: An Education Review Dataset for Aspect-based Sentiment Analysis Tasks

Every year, most educational institutions seek and receive an enormous volume of text feedback from students on courses, teaching, and overall experience. Yet, turning this raw feedback into useful insights is far from straightforward. It has been a long-standing challenge to adopt automatic opinion mining solutions for such education review text data due to the content complexity and low-granularity reporting requirements. Aspect-based Sentiment Analysis (ABSA) offers a promising solution with its rich, sub-sentence-level opinion mining capabilities. However, existing ABSA research and resources are very heavily focused on the commercial domain. In education, they are scarce and hard to develop due to limited public datasets and strict data protection. A high-quality, annotated dataset is urgently needed to advance research in this under-resourced area. In this work, we present EduRABSA (Education Review ABSA), the first public, annotated ABSA education review dataset that covers three review subject types (course, teaching staff, university) in the English language and all main ABSA tasks, including the under-explored implicit aspect and implicit opinion extraction. We also share ASQE-DPT (Data Processing Tool), an offline, lightweight, installation-free manual data annotation tool that generates labelled datasets for comprehensive ABSA tasks from a single-task annotation. Together, these resources contribute to the ABSA community and education domain by removing the dataset barrier, supporting research transparency and reproducibility, and enabling the creation and sharing of further resources. The dataset, annotation tool, and scripts and statistics for dataset processing and sampling are available at https://github.com/yhua219/edurabsa_dataset_and_annotation_tool.

cs.CL↗