Augmenting Student Startups’ Customer Validation Efforts Through Adaptive Coaching Using Large Language Models

Lois Abigail To, Zachary Matthew Alabastro, Joseph Benjamin Ilagan, Jose Ramon Ilagan

In HCI in Business, Government and Organizations (HCII 2025), LNCS, pp. 209–220. Springer, 2025.

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Abstract

Startup founders experience numerous barriers to venture creation. Customer development (CD), under the Lean Startup (LS) methodology, is a customer-centric process of refining business models to reduce startup failure. This paper explores the design of an LLM-based, adaptive coaching model that utilizes artificial intelligence’s (AI) natural language processing (NLP) and continuous learning capabilities to formulate contextual responses that are suitable for the user’s business context. Subsequently, the model generates synthetic customer insights to simulate customer interactions and strengthen CD competencies, creating more efficient systems for startup validation. Results suggest that the LLM-based adaptive coaching model is effective for startup validation and entrepreneurial education.

Keywords: Large language models; artificial intelligence; natural language processing; continuous learning; LLM adaptive coaching; LLM simulation; customer development; entrepreneurship

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Accepted manuscript. This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-031-92823-9_14. Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms

Cite as

Lois Abigail To, Zachary Matthew Alabastro, Joseph Benjamin Ilagan, Jose Ramon Ilagan (2025). Augmenting Student Startups’ Customer Validation Efforts Through Adaptive Coaching Using Large Language Models. In HCI in Business, Government and Organizations (HCII 2025), LNCS, pp. 209–220. Springer. https://doi.org/10.1007/978-3-031-92823-9_14