AI-Supported Learning for Relational Capital Development: A Case Study in Higher Education

Abstract

The systematic development of Relational Capital as part of Intellectual Capital is a critical element for career success. In two Austrian Master’s programs in digital technology and innovation at FHWien der WKW, an innovative teaching approach integrated AI-assisted learning with individual coaching. A customized GPT model was deployed as a structured reflection tool, assisting students in assessing and strategically developing their professional networks over the course duration. While traditional lecture time was reduced to two hours, instructional resources were redirected to AI-assisted learning and to group-coaching to enhance learning outcomes. This case study examines the use of generative AI as a dialogic tutor in a master's program on digital economy. Drawing on anonymized AI-student transcripts, coaching reflections, and structured prompts, the paper explores how AI supports strategic development of Relational Capital. Findings indicate high learner acceptance, evolving trust strategies, and improved metacognitive learning outcomes. AI shows strengths in guiding students through structured career planning exercises. However, concerns over trust and confidentiality emerged and lead students to adopt anonymization techniques to protect sensitive professional relationships. Beyond Relational Capital development, this study also highlights key pedagogical implications. AI’s effectiveness depends on high-quality learning materials, and students required training in prompt engineering to navigate information overload. The role of educators shifted towards facilitating learning processes, mentorship and strategic guidance, focusing on critical engagement with AI-generated content. This case study contributes to the discourse on AI integration in higher education. It demonstrates its role not only in developing Relational Capital but also in shaping self-directed learning behaviors and pedagogical innovation.