Paper Type
ERF
Abstract
With the growing advancements of artificial intelligence in recent years, the potential for practical applications and enhanced learning opportunities using generative AI tools is vast. As business college students navigate through their career journey, they often face challenges in fully understanding the practicality of their courses and how they translate into their future career. AI-powered career development tools assist business students by offering personalized guidance, job application strategies, and career-related resources. We designed and deployed Aspire AI, an application with generative AI integration that allows users to input their courses and career-relevant interests to receive business career opportunities that are tailored to their preferences. Through a user-centered evaluation with 32 undergraduate students in information systems, we measured the effectiveness of AI features in providing business students with relevant, practical career advice. We discuss insights gained from preliminary testing feedback as we aim to support long-term educational growth using generative AI-driven applications.
Paper Number
1876
Recommended Citation
Huynh, Kiley; Chen, Yu; and Hill, Timothy R., "Aspire AI: Empowering MIS Career Guidance with Generative AI" (2025). AMCIS 2025 Proceedings. 2.
https://aisel.aisnet.org/amcis2025/is_education/is_education/2
Aspire AI: Empowering MIS Career Guidance with Generative AI
With the growing advancements of artificial intelligence in recent years, the potential for practical applications and enhanced learning opportunities using generative AI tools is vast. As business college students navigate through their career journey, they often face challenges in fully understanding the practicality of their courses and how they translate into their future career. AI-powered career development tools assist business students by offering personalized guidance, job application strategies, and career-related resources. We designed and deployed Aspire AI, an application with generative AI integration that allows users to input their courses and career-relevant interests to receive business career opportunities that are tailored to their preferences. Through a user-centered evaluation with 32 undergraduate students in information systems, we measured the effectiveness of AI features in providing business students with relevant, practical career advice. We discuss insights gained from preliminary testing feedback as we aim to support long-term educational growth using generative AI-driven applications.
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