Denis Gillet

Biographie

Denis Gillet received the Diploma in Electrical Engineering from the Swiss Federal Institute of Technology in Lausanne (EPFL) in 1988, and the Ph.D. degree in Information Systems also from the EPFL in 1995. During 1992 he was appointed as Research Fellow at the Information Systems Laboratory of Stanford University in the United States. He is currently Maître d'enseignement et de recherche at the EPFL School of Engineering, where he leads the React research group. His current research interests include Technologies Enhanced Learning (TEL), Human Computer Interaction (HCI), Human Devices Interaction (HDI) and Optimal Coordination of Complex and Distributed Systems. Denis Gillet is affiliated at EPFL with the Center for Intelligent Systems and the Center for Digital Education.

Official source

https://people.epfl.ch/denis.gillet?lang=fr

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Over the past few years, there has been an increase in the use of chatbots for educational purposes. Nevertheless, the chatbot technologies and architectures that are often applied to educational contexts are not necessarily designed for such contexts. While general-purpose chatbot technologies can be used in educational contexts, there are some challenges specific to these contexts that need to be taken into consideration. Namely, chatbot technologies intended for education should, by design, integrate directly within online learning applications and focus on achieving learning goals by supporting learners with the task at hand. In this paper, we propose a blueprint for an architecture specifically aimed at integrating task-oriented chatbots to support learners in educational contexts. We then present a proof-of-concept implementation of our blueprint as a part of a code review application designed to teach programming best practices. Our blueprint could serve as a starting point for developers in education looking to build chatbot technologies targeting educational contexts and is a first step toward an open chatbot architecture explicitly tailored for learning applications.

2022

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A Canvas for the Ethical Design of Learning Experiences with Digital Tools

Pascal Felber, Denis Gillet, Cécile Hardebolle, Adrian Christian Holzer, Patrick Jermann, Roland John Tormey

The use of digital tools has drastically increased in engineering education, accelerated by the COVID-19 pandemic. These tools generate important ethical issues, in particular in terms of privacy and fairness. However, very few teacher training programmes address those topics, which means that teachers are often left to figure out by themselves how to address these issues when they want (or have) to use digital tools in their teaching. In this workshop, participants will be introduced to a pragmatic approach to the ethical design of learning experiences that involve digital tools using a visual thinking guide called a ‘canvas’. Applied and hands-on, this workshop will help participants to develop a practical understanding of the specific ethical issues related to the use of digital tools in teaching and to integrate ethical reflection into design processes when digital technology is involved.

2022

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An Exploratory Study of Reactions to Bot Comments on GitHub

Juan Carlos Farah, Denis Gillet, Sandy Ingram, Xinyang Lu

The widespread use of bots to support software development makes social coding platforms such as GitHub a particularly rich source of data for the study of human-bot interaction. Software development bots are used to automate repetitive tasks, interacting with their human counterparts via comments posted on the various discussion interfaces available on such platforms. One type of interaction supported by GitHub involves reacting to comments using predefined emoji. To investigate how users react to bot comments, we conducted an observational study comprising 54 million GitHub comments, with a particular focus on comments that elicited the laugh reaction. The results from our analysis suggest that some reaction types are not equally distributed across human and bot comments and that a bot's design and purpose influence the types of reactions it receives. Furthermore, while the laugh reaction is not exclusively used to express laughter, it can be used to convey humor when a bot behaves unexpectedly. These insights could inform the way bots are designed and help developers equip them with the ability to recognize and recover from unanticipated situations. In turn, bots could better support the communication, collaboration, and productivity of teams using social coding platforms.

ACM2022

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