Unfolding the Potential of Generative Artificial Intelligence: Design Principles for Chatbots in Academic Teaching and Research

Unfolding the Potential of Generative Artificial Intelligence: Design Principles for Chatbots in Academic Teaching and Research

Severin Bonnet (Osnabrück University, Germany) and Frank Teuteberg (Osnabrück University, Germany)
Copyright: © 2025 |Pages: 25
DOI: 10.4018/IJKM.368223
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Abstract

Scholars are increasingly using generative artificial intelligence (AI) chatbots, like ChatGPT, in research, though concerns remain about ethics, data privacy, bias, and intellectual property. This study adopts a design science research approach to explore how generative AI chatbots can support academic teaching and research, bridging theory and practice. A literature review and expert interviews identified key requirements and design principles that support virtues such as uniqueness, generalizability, and reproducibility. We also introduce a prototype, “AcademiaBot,” to demonstrate these principles in action. Our findings suggest that AI chatbots can significantly aid scholarly work if users are informed and ethical concerns are addressed. Responsible usage can help AI augment human research efforts without compromising integrity. This study provides valuable design knowledge, ensuring AI-based chatbots remain a beneficial tool for scholars.
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Introduction

Recent progress in generative artificial intelligence (AI) chatbots, such as ChatGPT, have unlocked new possibilities for content creation across various mediums, including academic teaching and research.1 This article focuses on how generative AI can be meaningfully integrated into academic settings. For instance, in the future it may be possible generative AI tools to reduce the time required to complete academic papers from several months to a few weeks. However, this raises questions about scholarly approaches that could be implemented alongside AI tools. Is such efficiency achievable without compromising the rigor of established scientific methods? ChatGPT, currently the most prominent generative AI chatbot, has fascinated scholars across various disciplines and has gained such popularity that it is likely to be “at capacity right now” (Dwivedi et al., 2023, p. 1). Its launch by OpenAI amplified the capabilities of chatbots via the integration of deep learning and large language models (LLMs) based on the generative pre-training transformer (GPT) architecture (Dwivedi et al., 2023). While machine learning excels in pattern recognition tasks, ChatGPT goes one step further by taking advantage of these patterns to generate new data. This generative capability could increase the productivity of scientific research, enabling scholars to dedicate more time to advanced cognitive tasks (Dwivedi et al., 2023). Generative AI chatbots can engage in human-like dialogues and learn from the data collected. Their advanced language comprehension and ability to process human interactions allow nuanced conversations that draw on extensive experiential knowledge and empathy (Luo et al., 2022). The emergence of these tools brings considerable potential to upgrade existing research processes and systems. The disruption phenomenon has already been witnessed in various domains, such as academia, which is embracing digital transformation. Thus, the significance of generating academic publications may soon shift, emphasizing the need to ask and answer meaningful and novel research questions in an increasingly algorithm-driven world (Dwivedi et al., 2023). Tools such as SciNote Manuscript Writer further demonstrate the impact of AI in academic teaching and research, as they can generate comprehensive papers in medical research (Gao et al., 2023).2 Moreover, the studies by Urban et al. (2024) and Lin et al. (2023) showed generative AI´s benefits in terms of fostering personalized learning environments and enhancing inclusivity in academic settings.

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