{"id":2253,"date":"2025-04-10T15:24:34","date_gmt":"2025-04-10T13:24:34","guid":{"rendered":"https:\/\/wp.unil.ch\/aail\/?p=2253"},"modified":"2025-07-05T16:11:48","modified_gmt":"2025-07-05T14:11:48","slug":"desi-seminar-design-and-effects-of-human-centered-ai-based-writing-systems-for-education","status":"publish","type":"post","link":"https:\/\/wp.unil.ch\/aail\/desi-seminar-design-and-effects-of-human-centered-ai-based-writing-systems-for-education\/","title":{"rendered":"DESI Seminar: Design and Effects of Human-Centered AI-based Writing Systems for Education"},"content":{"rendered":"\n
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We are excited to announce that Prof. Thiemo Wambsganss from the Bern University of Applied Sciences, will be delivering a seminar on Design and Effects of Human-Centered AI-based Writing Systems for Education<\/strong>.<\/p>\n\n\n\n

Abstract:<\/strong> In his talk, Dr. Thiemo Wamsbganss will present his research at the intersection of Human-Computer Interaction, Natural Language Processing, and Educational Technology on interactive writing assistants. In particular, he will present two projects: 1) his research on argumentation writing support systems for students to learn argumentation skills at scale with adaptive feedback and 2) his recent studies on conversational writing systems for reflective writing skills.

Argumentation is an omnipresent rudiment of daily communication and thinking. The ability to form convincing arguments is not only fundamental to persuading an audience of novel ideas but also plays a major role in strategic decision-making, negotiation, and constructive civil discourse. However, humans often struggle to develop argumentation skills owing to a lack of individual and instant feedback in their learning process since providing feedback on the individual argumentation skills of learners is time-consuming and not scalable if conducted manually by educators. Building on social cognitive theory, he investigates a set of theory-based design principles for a novel class of AI-based argumentation writing systems. The proposed systems provide learners with adaptive self-evaluation opportunities based on logical argumentation errors irrespective of instructor, time, and location. To evaluate the principles\u2019 feasibility, he instantiates, develops, demonstrates, and evaluates his  AI-based argumentation learning system ArgueLearn in a laboratory and field experiment. Based on this research, he followed up to investigate the potential of conversational tutoring and adaptive feedback for reflective writing of students and will present his latest research on a reflective writing system called MindBuddy, which was evaluated in two classroom studies with vocational students. <\/p>\n\n\n\n

Short bio: <\/strong>Prof. Dr. Thiemo Wambsganss is a Tenure-Track Research Assistant Professor at Bern University of Applied Science in Bern, CH, and head of the Human-Centered AI-based Learning Systems (HAIS) Lab. His research lies in the general area of Human-Computer Interaction with influences from Natural Language Processing and Machine Learning, with a focus on educational systems. <\/p>\n\n\n\n

He strives\u00a0to understand how\u00a0humans perceive,\u00a0interact, and\u00a0learn\u00a0with\u00a0intelligent\u00a0tools\u00a0to\u00a0build\u00a0and\u00a0study\u00a0innovative,\u00a0human-centered learning\u00a0systems that enhance (digital) education. Specifically, He is driven by the potential of advancements in\u00a0Natural Language Processing,\u00a0Machine Learning, and\u00a0novel interfaces\u00a0(e.g.,\u00a0AR\/VR, conversational agents) to support key educational activities such as\u00a0reading,\u00a0writing, and\u00a0assessment. To achieve this, he used advanced techniques such as\u00a0Argumentation Mining,\u00a0Empathy Modeling, and the integration of\u00a0Large Language Models\u00a0into adaptive learning tools. His\u00a0research follows three connected main lines of work: 1)\u00a0Designing\u00a0and\u00a0evaluating\u00a0conversational agents\u00a0and\u00a0intelligent writing assistants\u00a0to improve learning outcomes and engagement. 2) Developing\u00a0adaptive,\u00a0spatial, and\u00a0interactive interfaces\u00a0that\u00a0personalize\u00a0and\u00a0scale learning\u00a0experiences based on\u00a0computational modeling. 3) Examining the\u00a0ethical implications\u00a0of AI-based learning tools, including\u00a0trust in AI feedback, mitigation of\u00a0bias, and\u00a0value-sensitive (more information\u00a0thiemowa.github.io<\/a>).<\/p>\n\n\n\n

<\/p>\n\n\n\n

<\/p>\n","protected":false},"excerpt":{"rendered":"

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