Eduard Frankford

dblp:374/8779 · DBLP profile ↗
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5ranked-venue papers
4as first author
5since 2021 · last 2026
0009-0005-5959-4936ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 5 · 4 first-author · 5 since 2021Applied, interdisciplinary, general and emerging computing · 3 · 3 first-author · 3 since 2021
YearPublicationVenuePosition
2026 Chatbot-Based Assessment of Code Understanding in Automated Programming Assessment Systems
Eduard Frankford, Erik Cikalleshi, Ruth Breu
CSEDU (1)1
2025 An Online Integrated Development Environment for Automated Programming Assessment Systems
Eduard Frankford, Daniel Crazzolara, Michael Vierhauser, Niklas Meißner, Stephan Krusche, Ruth Breu
CSEDU (1)1
2024 Requirements for an Online Integrated Development Environment for Automated Programming Assessment Systems
Eduard Frankford, Daniel Crazzolara, Clemens Sauerwein, Michael Vierhauser, Ruth Breu
CSEDU (1)1
2024 A Survey Study on the State of the Art of Programming Exercise Generation Using Large Language Models
abstract
This paper analyzes Large Language Models (LLMs) with regard to their programming exercise generation capabilities. Through a survey study, we defined the state of the art, extracted their strengths and weaknesses and finally proposed an evaluation matrix, helping researchers and educators to decide which LLM is the best fitting for the programming exercise generation use case. We also found that multiple LLMs are capable of producing useful program-ming exercises. Nevertheless, there exist challenges like the ease with which LLMs might solve exercises generated by LLMs. This paper contributes to the ongoing discourse on the integration of LLMs in education.
Eduard Frankford, Ingo Höhn, Clemens Sauerwein, Ruth Breu
CSEE&T1
2024 Iris: An AI-Driven Virtual Tutor for Computer Science Education
abstract
Integrating AI-driven tools in higher education is an emerging area with transformative potential. This paper introduces Iris, a chat-based virtual tutor integrated into the interactive learning platform Artemis that offers personalized, context-aware assistance in large-scale educational settings. Iris supports computer science students by guiding them through programming exercises and is designed to act as a tutor in a didactically meaningful way. Its calibrated assistance avoids revealing complete solutions, offering subtle hints or counter-questions to foster independent problem-solving skills. For each question, it issues multiple prompts in a Chain-of-Thought to GPT-3.5-Turbo. The prompts include a tutor role description and examples of meaningful answers through few-shot learning. Iris employs contextual awareness by accessing the problem statement, student code, and automated feedback to provide tailored advice. An empirical evaluation shows that students perceive Iris as effective because it understands their questions, provides relevant support, and contributes to the learning process. While students consider Iris a valuable tool for programming exercises and homework, they also feel confident solving programming tasks in computer-based exams without Iris. The findings underscore students' appreciation for Iris' immediate and personalized support, though students predominantly view it as a complement to, rather than a replacement for, human tutors. Nevertheless, Iris creates a space for students to ask questions without being judged by others.
Patrick Bassner, Eduard Frankford, Stephan Krusche
ITiCSE (1)2