The CAM-CAI-RCHE Model: An Adoption Model for Conversational AI in Career Guidance for Resource-Constrained Higher Education Settings

Abstract

Conversational Artificial Intelligence (AI) offers a significant opportunity to enhance career guidance services in higher education, particularly in resource-limited settings like Ugandan universities. However, effectively deploying such technologies requires a nuanced understanding of local challenges and contextual factors often overlooked by generic adoption frameworks. This paper introduces the Contextualized Adoption Model for Conversational AI in Resource-Constrained Higher Education (CAM-CAI-RCHE), a conceptual model that provides actionable steps for the effective adoption of AI-powered conversational career guidance tools. The model integrates key principles from established adoption literature with specific needs for these unique environments, including comprehensive stakeholder involvement, customization with local data, principled AI ethics, provision for multi-channel access, careful integration into academic practices, and strategies for building sustainable local capacity. By offering a structured, context-sensitive pathway, this model seeks to overcome the limitations of broader theories, enhance adoption outcomes, and ultimately leverage conversational AI to significantly benefit student career development in Uganda and similar resource-constrained contexts.

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