On November 30th, 2022, OpenAI released the large language model ChatGPT, an extension of GPT-3. The AI chatbot provides real-time communication in response to users’ requests. The quality of ChatGPT’s natural speaking answers marks a major shift in how we will use AI-generated information in our day-to-day lives. For a software engineering student, the use cases for ChatGPT are manifold: assessment preparation, translation, and creation of specified source code, to name a few. It can even handle more complex aspects of scientific writing, such as summarizing literature and paraphrasing text. Hence, this position paper addresses the need for discussion of potential approaches for integrating ChatGPT into higher education. Therefore, we focus on articles that address the effects of ChatGPT on higher education in the areas of software engineering and scientific writing. As ChatGPT was only recently released, there have been no peer-reviewed articles on the subject. Thus, we performed a structured grey literature review using Google Scholar to identify preprints of primary studies. In total, five out of 55 preprints are used for our analysis. Furthermore, we held informal discussions and talks with other lecturers and researchers and took into account the authors’ test results from using ChatGPT. We present five challenges and three opportunities for the higher education context that emerge from the release of ChatGPT. The main contribution of this paper is a proposal for how to integrate ChatGPT into higher education in four main areas.
Context: Higher education is changing at an accelerating pace due to the widespread use of digital teaching and emerging technologies. In particular, AI assistants such as ChatGPT pose significant challenges for higher education institutions because they bring change to several areas, such as learning assessments or learning experiences.
Objective: Our objective is to discuss the impact of AI assistants in the context of higher education, outline possible changes to the context, and present recommendations for adapting to change.
Method: We review related work and develop a conceptual structure that visualizes the role of AI assistants in higher education.
Results: The conceptual structure distinguishes between humans, learning, organization, and disruptor, which guides our discussion regarding the implications of AI assistant usage in higher education. The discussion is based on evidence from related literature.
Conclusion: AI assistants will change the context of higher education in a disruptive manner, and the tipping point for this transformation has already been reached. It is in our hands to shape this transformation.
In diesem Beitrag werden Spezifika der Hochschulen und Ausbildungseinrichtungen, die in der KIBA organisiert sind, mit ihren Studiengängen, Weiterbildungsprogrammen, Forschungsschwerpunkten und didaktischen Konzepten vorgestellt. Es wird gezeigt, wie diese Einrichtungen mit ihrer Berufungs- und Einstellungspolitik, strategischen Allianzen und übergeordneten fachlichen und politischen Zusammenschlüssen sowie mit der Profilierung ihrer Studiengänge auf neue Anforderungen des Marktes und der Berufspraxis reagieren. Berücksichtigt werden dabei Positionen und Strategien zur Digitalisierung aus der Politik sowie ihren Beratungsgremien, in der sich die Inhalte bibliotheks- und informationswissenschaftlicher Ausbildung und Forschung verorten lassen. Insgesamt wird deutlich, wie schwierig es heute ist zu definieren, was die Bibliotheks- und die Informationswissenschaft im Kern ausmacht, um im Spannungsfeld der Herausforderungen an wissenschaftliche und öffentliche Bibliotheken, den Anforderungen der Wirtschaft im Bereich Informations- und Wissensmanagement, der Digitalisierung und Langzeitarchivierung von Kulturerbe, um nur einige Felder zu nennen, Ausbildungsprogramme bedarfsgerecht zu profilieren und die bibliotheks- und informationswissenschaftlichen Institute, Fachbereiche und Ausbildungseinrichtungen politisch abzusichern und ausreichend mit Ressourcen auszustatten.