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AI-Powered Adaptive Education Research

Developing AI-powered adaptive learning tools.

We conduct interdisciplinary research in the field of information systems, computer science and human-computer-interaction to increase learning success based on ai-powered education tools such as conversational agents, smart personal assistants or chatbots.

Current publications and projects

AL: An Adaptive Learning Support System for Argumentation Skills

Recent advances in Natural Language Processing (NLP) bear the opportunity to analyze the argumentation quality of texts. This can be leveraged to provide students with individual and adaptive feedback in their personal learning journey. To test if individual feedback on students’ argumentation will help them to write more convincing texts, we developed AL, an adaptive IT tool that provides students with feedback on the argumentation structure of a given text. We compared AL with 54 students to a proven argumentation support tool. We found students using AL wrote more convincing texts with better formal quality of argumentation compared to the ones using the traditional approach. The measured technology acceptance provided promising results to use this tool as a feedback application in different learning settings. The results suggest that learning applications based on NLP may have a beneficial use for developing better writing and reasoning for students in traditional learning settings.

Published at CHI2020 in Honolulu.

Sara, the Lecturer: Improving Learning in Online Education with a Scaffolding-Based Conversational Agent

Enrollment in online courses has sharply increased in higher education. Although online education can be scaled to large audiences, the lack of interaction between educators and learners is difficult to replace and remains a primary challenge in the field. Conversational agents may alleviate this problem by engaging in natural interaction and by scaffold- ing learners’ understanding similarly to educators. However, whether this approach can also be used to enrich online video lectures has largely remained unknown. We developed Sara, a conversational agent that appears during an online video lecture. She provides scaffolds by voice and text when needed and includes a voice-based input mode. An evaluation with 182 learners in a 2 x 2 lab experiment demonstrated that Sara, compared to more traditional conversational agents, significantly improved learning in a programming task. This study highlights the importance of including scaffolding and voice-based conversational agents in online videos to improve meaningful learning.

Published at CHI2020 in Honolulu.

A Conversational Agent to Improve Response Quality in Course Evaluations

A Conversational Agent to Improve Response Quality in Course Evaluations

Recent advances in Natural Language Processing (NLP) bear the opportunity to design new forms of human-computer interaction with conversational interfaces. We hypothesize that these interfaces can interactively engage students to increase response quality of course evaluations in education compared to the common standard of web surveys.Past research indicates that web surveys come with disadvantages, such as poor response quality caused by inattention, survey fatigue or satisficing behavior. To test if conversational interfaces have a positive impact on the level of enjoyment and the response quality, we design an NLP- based conversational agent and deploy it in a field experiment with 127 students in our lecture and compare it with a web survey as a baseline. Our findings indicate that using conversational agents for evaluations are resulting in higher levels of response quality and level of enjoyment, and are therefore, a promising approach to increase the effective- ness of surveys in general.

Published at CHI2020 in Honolulu as Late Breaking Work.

Interested in a research collaboration:

Institute of Information Management

University of St. Gallen

Müller-Friedberg-Strasse 8

9000 St. Gallen, Switzerland

Contact us

Copyright Institute of Information Management, University of St. Gallen, Switzerland. All rights reserved.

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