Enhancing Understanding of AI-Based Digital Business Through Interactive Seminars for Information Technology Students
DOI:
https://doi.org/10.63822/vk045k91Keywords:
Business Model Canvas, Entrepreneurship, Digital BusinessAbstract
The development of artificial intelligence (AI) technology has become a major driver in the transformation of the digital business world, including in the startup sector. However, a deep understanding of AI integration into business models remains a challenge for students, particularly in the field of Information Technology (IT). This community service activity aims to enhance the knowledge and skills of IT students in designing strategic, ethical, and sustainable AI-based digital businesses. The implementation method involves a one-day educational seminar, including presentations, interactive discussions, simulations of Business Model Canvas (BMC) development, and evaluation through questionnaires. Evaluation results showed significant improvements: understanding of the BMC increased from 41% to 89%, understanding of AI startup concepts from 54% to 92%, ability to draft a business plan from 16% to 78%, and motivation for technology entrepreneurship from 68% to 90%. These findings indicate that an applied and participatory approach in seminars is effective in developing digital entrepreneurship capacity among IT students.
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References
Al-Mamary, M. (2025). Artificial intelligence applications in business operations: Enhancing personalization and efficiency. Journal of Business and Artificial Intelligence, 1(2). https://jbai.ai
Amalia, R., Paradita, J., Aini, R. Q., Sudrajat, S., & Mirza, A. D. (2024). Transforming business with AI: Impacts, challenges and opportunities. In Proceedings of the 7th International Conference of Economics, Business, and Entrepreneurship (ICEBE 2024). https://doi.org/10.4108/eai.4-9-2024.2353765
Cui, P., & Alias, B. S. (2024). Opportunities and challenges in higher education arising from AI: A systematic literature review (2020–2024). Journal of Intelligent Processes and Data, 8(11). https://systems.enpress-publisher.com/index.php/jipd/article/view/8390
Domini, F., Giuggioli, G., & Pellegrini, M. M. (2023). Artificial intelligence as an enabler for entrepreneurs: A systematic literature review and an agenda for future research. International Journal of Entrepreneurial Behavior & Research, 29(4), 816–837. https://doi.org/10.1108/IJEBR-05-2021-0426
Font-Cot, J., et al. (2023). Strategic growth of AI startups. Administrative Sciences, 14(1), Article 6. https://www.mdpi.com/2076-3387/14/1/6
Gelashvili-Luik, T., Vihma, P., & Pappel, I. (2025). Navigating the AI revolution: Challenges and opportunities for integrating emerging technologies into knowledge management systems. Frontiers in Artificial Intelligence, 8. https://doi.org/10.3389/frai.2025.1595930
Hanifzadeh, M., Ghasemi, Z., Nobari, N., & Seraj, M. (2024). Artificial intelligence adoption by digital startups in decision-making within uncertain business environments. In Entrepreneurship – Digital Transformation, Education, Opportunities and Challenges. https://doi.org/10.5772/intechopen.1007080
Jain, C., & Kanwar, J. (2025). The AI-driven workplace: How automation is reshaping flexible work arrangements. Journal of Information Systems Engineering and Management, 10(25s). https://doi.org/10.52783/jisem.v10i25s.4097
Kasireddy, L. C., & Sreenivasu, M. (2025). Overcoming adoption barriers: Strategies for scalable AI transformation in enterprises. Journal of Informatics Education and Research, 5(2). https://doi.org/10.52783/jier.v5i2.2459
Nitsch, V., Rick, V., Kluge, A., & Wilkens, U. (2024). Human-centered approaches to AI-assisted work: The future of work?. Zeitschrift für Arbeitswissenschaft, 78, 261–267. https://doi.org/10.1007/s41449-024-00437-2
Ogundipe, A., & Abaku, M. (2024). AI readiness in emerging markets. Journal of Artificial Intelligence for Sustainable Development.
Perifanis, N.-A., & Kitsios, F. (2023). Investigating the influence of artificial intelligence on business value in the digital era of strategy: A literature review. Information, 14(2), 85. https://doi.org/10.3390/info14020085
Radhakrishnan, J., & Chattopadhyay, M. (2020). Determinants and barriers of artificial intelligence adoption – A literature review. In IFIP Advances in Information and Communication Technology (Vol. 617, pp. 89–99). https://link.springer.com/chapter/10.1007/978-3-030-64849-7_9
Talebi, K., Ghasemi, Z., Nobari, N., & Seraj, M. (2025). Artificial intelligence adoption by digital startups in decision-making within uncertain business environments. IntechOpen. https://doi.org/10.5772/intechopen.1007080
Uriarte, E., et al. (2025). Business model innovation using BMC and AI. Sustainability, 17(7), Article 5373. https://www.mdpi.com/2071-1050/17/7/5373
Uriarte, S., Baier-Fuentes, H., Espinoza-Benavides, J., & Inzunza-Mendoza, W. (2025). Artificial intelligence technologies and entrepreneurship: A hybrid literature review. Review of Managerial Science. https://doi.org/10.1007/s11846-025-00839-4
Wynsberghe, A. (2021). Sustainable AI: AI for sustainability and the sustainability of AI. AI and Ethics.
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Copyright (c) 2025 Ade Sarah Huzaifah, Rossy Nurhasanah, Fanindia Purnamasari, Dedy Arisandi, Ivan Jaya (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
