Abstract:

The rapid integration of Artificial Intelligence (AI) into higher education is fundamentally altering student learning behaviors. As large language models provide instant access to context and conceptual understanding, students increasingly question the necessity of traditional knowledge internalization. This article explores if the ability of AI, which may replace a large part of human intellectual work in the future, may even become a barrier to self-directed learning and deep mastery of academic content. This is the main research question to be investigated.

From an educator's perspective, this shift raises two critical questions:
1. Is the cultivation of traditional competencies still essential, and if so, how can learning outcomes be reliably evaluated?
2. Given the high administrative burden and susceptibility to fraud of unsupervised written assessments, this paper proposes oral examinations as the most effective alternative for assessing authentic student understanding and knowledge transfer.

The primary objective of this research is to analyze the feasibility of oral exams in the AI era and to identify correlative relationships between openness for oral examinations and various factors such as demographics, personality traits, technological proficiency, academic motivation, and career aspirations. The findings aim to provide a framework for effective evaluation methods in an increasingly digitized educational landscape. The results of the study will be presented at the conference.

Sample and data collection process: Data will be obtained from students on bachelor and master level courses in Business Administration and Business Engineering from various higher educational institutions in Germany. A questionnaire is used to collect the data. The advantage of a questionnaire strategy is that it provides standardized answers that make it simple to compile data. Because the topic of the paper is a very complex one, different quantitative (5-point Likert scales, with an additional field “don’t know”) data are collected to triangulate findings. The collected data are analyzed with the objective of assessing the effectiveness of oral examinations. Furthermore, links between study performance, willingness to acquire and internalize knowledge and career ambitions will be analyzed. The data are analyzed by SPSS and MS Excel.

Keywords:

Artificial Intelligence, AI, examination methods, oral examinations, internalization of knowledge, transformation of teaching, digital transformation.