arXiv subjects

Shin'ichi Konomi

Publications and source records attributed to Shin'ichi Konomi.

8 recordsLinked to original sources

arXiv2026

AI-Assisted Help-Seeking Trajectories in Programming Education from an SRL-Informed Perspective

Generative AI tools provide novice programmers with instant, personalized support, but also raise concerns about whether AI use supports or bypasses students' regulation of problem-solving. Existing work has largely focused on correctness, usability, or overall usage frequency, with less attention to how student--AI help-seeking unfolds. This study addresses this gap by analyzing AI-assisted help-seeking trajectories in university-level programming. Using an SRL-informed analytical framework that links prompt-level help-seeking codes to conceptual, implementation, debugging, and reflective forms of support, we analyzed 1,290 task-specific student prompts linked to 17,190 code submissions from 71 students in introductory Python programming courses. Specifically, we examined how help-seeking interactions were structured across turns and attempts, and how trajectory patterns related to task scores and the number of code submissions. Results indicate that many students primarily used AI for reactive troubleshooting rather than for planned, self-regulated problem-solving. Although trajectory patterns were not associated with significant differences in task scores, they differed substantially in the number of code submissions required. These findings suggest that the educational significance of AI support lies not only in whether students use AI, but in how their help-seeking trajectories develop during programming problem-solving.

cs.AI↗

arXiv2026

Three Years with Classroom AI in Introductory Programming: Shifts in Student Awareness, Interaction, and Performance

Generative AI (GenAI) tools such as ChatGPT now provide novice programmers with instant, personalized support and are reshaping computing education. While a growing body of work examines AI's immediate impacts, longitudinal evidence remains limited on how students' awareness, student-AI interaction patterns, and course outcomes evolve as AI becomes routine in classrooms. To address this gap, we investigate an introductory Python course across three successive AI-supported cohorts (2023-2025). Using questionnaires, coded student-AI dialogue logs, and course assessment records, we examine cohort-to-cohort shifts in students' AI awareness, interaction practices, and learning outcomes. We find that students' relationships with GenAI change systematically over time: familiarity and uptake become increasingly normative, and help-seeking practices evolve alongside growing AI literacy and shifting expectations of what the assistant should provide. These changes suggest that, in the AI era, the central instructional challenge is less about whether students use AI and more about how courses redefine productive learning practices while maintaining student agency. Our study offers longitudinal evidence and practical implications for designing and integrating AI programming support in course settings.

cs.HC↗

arXiv2025

Scaffolding Metacognition in Programming Education: Understanding Student-AI Interactions and Design Implications

Generative AI tools such as ChatGPT now provide novice programmers with unprecedented access to instant, personalized support. While this holds clear promise, their influence on students' metacognitive processes remains underexplored. Existing work has largely focused on correctness and usability, with limited attention to whether and how students' use of AI assistants supports or bypasses key metacognitive processes. This study addresses that gap by analyzing student-AI interactions through a metacognitive lens in university-level programming courses. We examined more than 10,000 dialogue logs collected over three years, complemented by surveys of students and educators. Our analysis focused on how prompts and responses aligned with metacognitive phases and strategies. Synthesizing these findings across data sources, we distill design considerations for AI-powered coding assistants that aim to support rather than supplant metacognitive engagement. Our findings provide guidance for developing educational AI tools that strengthen students' learning processes in programming education.

cs.HC↗

arXiv2025

How Good Are Large Language Models for Course Recommendation in MOOCs?

Large Language Models (LLMs) have made significant strides in natural language processing and are increasingly being integrated into recommendation systems. However, their potential in educational recommendation systems has yet to be fully explored. This paper investigates the use of LLMs as a general-purpose recommendation model, leveraging their vast knowledge derived from large-scale corpora for course recommendation tasks. We explore a variety of approaches, ranging from prompt-based methods to more advanced fine-tuning techniques, and compare their performance against traditional recommendation models. Extensive experiments were conducted on a real-world MOOC dataset, evaluating using LLMs as course recommendation systems across key dimensions such as accuracy, diversity, and novelty. Our results demonstrate that LLMs can achieve good performance comparable to traditional models, highlighting their potential to enhance educational recommendation systems. These findings pave the way for further exploration and development of LLM-based approaches in the context of educational recommendations.

cs.IR↗

arXiv2025

Leveraging the Dynamics of Leadership in Group Recommendation Systems

In the field of group recommendation systems (GRS), effectively addressing the diverse preferences of group members poses a significant challenge. Traditional GRS approaches often aggregate individual preferences into a collective group preference to generate recommendations, which may overlook the intricate interactions between group members. We introduce a novel approach to group recommendation, with a specific focus on small groups sharing common interests. In particular, we present a web-based restaurant recommendation system that enhances user satisfaction by modeling mutual interactions among group members. Drawing inspiration from group decision-making literature and leveraging graph theory, we propose a recommendation algorithm that emphasizes the dynamics of relationships and trust within the group. By representing group members as nodes and their interactions as directed edges, the algorithm captures pairwise relationships to foster consensus and improve the alignment of recommendations with group preferences. This interaction-focused framework ultimately seeks to enhance overall group satisfaction with the recommended choices.

cs.IR↗

arXiv2024

Enhancing Programming Education with ChatGPT: A Case Study on Student Perceptions and Interactions in a Python Course

The integration of ChatGPT as a supportive tool in education, notably in programming courses, addresses the unique challenges of programming education by providing assistance with debugging, code generation, and explanations. Despite existing research validating ChatGPT's effectiveness, its application in university-level programming education and a detailed understanding of student interactions and perspectives remain limited. This paper explores ChatGPT's impact on learning in a Python programming course tailored for first-year students over eight weeks. By analyzing responses from surveys, open-ended questions, and student-ChatGPT dialog data, we aim to provide a comprehensive view of ChatGPT's utility and identify both its advantages and limitations as perceived by students. Our study uncovers a generally positive reception toward ChatGPT and offers insights into its role in enhancing the programming education experience. These findings contribute to the broader discourse on AI's potential in education, suggesting paths for future research and application.

cs.CY↗

arXiv2014

Touch Survey: Comparison with Paper and Web Questionnaires

We developed a prototype of touch-based survey tool for tablets and conducted an experiment to compare interaction patterns of touch-based, PC-based, and paper-based questionnaires. Our findings suggest that a touch-based interface allows users to complete ranking questions easily, quickly, and accurately although it can increase the time to complete a location input task for well-known, prominent locations.

cs.HC↗

arXiv2014

Lost Again in Shibuya: Exploration and Awareness in a Labyrinth

Existing digital technologies in urban settings tend to focus narrowly on concerns around wayfinding, safety, and consumption. In this paper, we examine pedestrian experiences based on the data collected through field observations as well as intensive interviews with nine pedestrians in the Shibuya area of Tokyo, and suggest an alternative approach to blending technologies and urban activities. Our focus is on social and cognitive aspects of pedestrians who get lost and explore a labyrinth of sidewalks. We use the data to discuss the activities that are often ignored or inadequately supported by existing systems.

cs.HC↗