selected publications | Isaac Alpizar-Chacon

2025

Koli 2025

Excited, Skeptical, or Worried? A Multi-Institutional Study of Student Views on Generative AI in Computing Education

Isaac Alpizar-Chacon, Hieke Keuning, Imke Jong, and 2 more authors

In Proceedings of the 25th Koli Calling International Conference on Computing Education Research, , 2025

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The application of Artificial Intelligence, in particular Generative AI, has become more widespread among educational institutions. Opinions vary widely on whether integrating AI into classrooms is the way forward or if it is detrimental to the quality of education. Increasingly, research studies are giving us more insight into the consequences of using AI tools in learning and teaching. Studies have shown how, when, and why students use AI tools. Because developments regarding the technology and its use are moving fast, we need frequent, ongoing, and more fine-grained investigation. One aspect that we do not know much about yet is how students use and think about AI across different types of education. In this paper, we present the results of a multi-institutional survey with responses from 410 students enrolled in the computing programs of 23 educational institutions, representing high schools, colleges, and research universities. We found distinct usage patterns across the three educational institution types. Students from all types express excitement, optimism, and gratitude toward GenAI. Students in higher education more often report worry and skepticism, while high school students report greater trust and fewer negative feelings. Additionally, the AI hype has had a minimal influence, positive or negative, on high school students’ decision to pursue computing. Our study contributes to a better understanding of inter-institutional differences in AI usage and perception and can help educators and students better prepare for future challenges related to AI in computing education.
RESPECT 2025

Mapping Latin American Research in Computing Education: Participation and Disparities

Catalina Sánchez-Martı́nez and Isaac Alpizar-Chacon

In Proceedings of the 2025 Conference on Research on Equitable and Sustained Participation in Engineering, Computing, and Technology, Newark, NJ, USA, 2025

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Latin American researchers remain underrepresented in Computing Education Research (CER), accounting for only 1.6% of global participation. This study uses bibliometric analysis to map Latin America’s participation, collaboration patterns, and research themes in top international CER venues over five years. Findings reveal that only a few countries in the region are involved, with overall low research participation. Additionally, regional collaboration remains limited. These disparities suggest challenges to achieving equitable participation in global CER. Addressing these challenges requires inclusive research practices and stronger regional networks, ensuring Latin American voices help shape an accessible and equitable future in computing education.
ITiCSE 2025

Student’s Use of Generative AI as a Support Tool in an Advanced Web Development Course

Isaac Alpizar-Chacon and Hieke Keuning

In Proceedings of the 30th ACM Conference on Innovation and Technology in Computer Science Education V. 1, Nijmegen, Netherlands, 2025

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Various studies have studied the impact of Generative AI on Computing Education. However, they have focused on the implications for novice programmers. In this experience report, we analyze the use of GenAI as a support tool for learning, creativity, and productivity in a web development course for undergraduate students with extensive programming experience. We collected diverse data (assignments, reflections, logs, and a survey) and found that students used GenAI on different tasks (code generation, idea generation, etc.) with a reported increase in learning and productivity. However, they are concerned about over-reliance and incorrect solutions and want more training in prompting strategies.
ITiCSE 2024

A Pedagogical Framework for Developing Abstraction Skills

Marjahan Begum, Julia Crossley, Filip Strömbäck, and 8 more authors

In 2024 Working Group Reports on Innovation and Technology in Computer Science Education, Milan, Italy, 2025

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Abstraction is a fundamental yet challenging skill to teach and learn in Computer Science education. Traditional frameworks of abstraction and concept formation often emphasize understanding an abstraction over its application, the latter being critical for practical Computer Science. Additionally, a common issue in education is when students may understand a concept in a classroom or a very specific setting but struggle to apply it outside of that context. In response, we present here a novel pedagogical framework designed to enhance both the development and application of abstraction skills in diverse educational contexts within the field of Computer Science. Our framework synthesizes common themes from existing models while introducing a new dimension focused explicitly on the actionable development of abstraction skills. Educators can adapt the framework to various educational contexts to support development of students’ abstraction skills. Our framework was iteratively developed through a combination of theoretical analysis and reflective practice across multiple teaching contexts. We demonstrate the suitability of the framework by applying it to various case studies, demonstrating its broad applicability and practical utility. By offering a flexible yet comprehensive structure, our framework enables educators to effectively organize and deliver educational content, guiding students from abstract theoretical concepts to their practical application in Computer Science.

2024

Koli 2024

Students’ Perceptions and Use of Generative AI Tools for Programming Across Different Computing Courses

Hieke Keuning, Isaac Alpizar-Chacon, Ioanna Lykourentzou, and 4 more authors

In Proceedings of the 24th Koli Calling International Conference on Computing Education Research, , 2024

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Investigation of students’ perceptions and opinions on the use of generative artificial intelligence (GenAI) in education is a topic gaining much interest. Studies addressing this are typically conducted with large heterogeneous groups, at one moment in time. However, how students perceive and use GenAI tools can potentially depend on many factors, including their background knowledge, familiarity with the tools, and the learning goals and policies of the courses they are taking. In this study we explore how students following computing courses use GenAI for programming-related tasks across different programs and courses: Bachelor and Master, in courses in which learning programming is the learning goal, courses that require programming as a means to achieve another goal, and in courses in which programming is optional, but can be useful. We are also interested in changes over time, since GenAI capabilities are changing at a fast pace, and users are adopting GenAI increasingly. We conducted three consecutive surveys (fall ‘23, winter ‘23, and spring ‘24) among students of all computing programs of a large European research university. We asked questions on the use in education, ethics, and job prospects, and we included specific questions on the (dis)allowed use of GenAI tools in the courses they were taking at the time. We received 264 responses, which we quantitatively and qualitatively analyzed, to find out how students have employed GenAI tools across 59 different computing courses, and whether the opinion of an average student about these tools evolves over time. Our study contributes to the emerging discussion of how to differentiate GenAI use across different courses, and how to align its use with the learning goals of a computing course.

2021

Hyper and Multi

Knowledge models from PDF textbooks

Isaac Alpizar-Chacon and Sergey Sosnovsky

New Review of Hypermedia and Multimedia, 2021

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Textbooks are educational documents created, structured and formatted by domain experts with the primary purpose to explain the knowledge in the domain to a novice. Authors use their understanding of the domain when structuring and formatting the content of a textbook to facilitate this explanation. As a result, the formatting and structural elements of textbooks carry the elements of domain knowledge implicitly encoded by their authors. Our paper presents an extensible approach towards automated extraction of knowledge models from textbooks and enrichment of their content with additional links (both internal and external). The textbooks themselves essentially become hypertext documents where individual pages are annotated with important concepts in the domain. The evaluation experiments examine several aspects and stages of the approach, including the accuracy of model extraction, the pragmatic quality of extracted models using one of their possible applications— semantic linking of textbooks in the same domain, the accuracy of linking models to external knowledge sources and the effect of integration of multiple textbooks from the same domain. The results indicate high accuracy of model extraction on symbolic, syntactic, and structural levels across textbooks and domains, and demonstrate the added value of the extracted models on the semantic level.