Work-integrated learning (WIL) facilitates authentic skill-building toward career readiness, yet equal access and impact continues to present barriers. This paper aims to put forth an integrative framework incorporating generative AI to reimagine more personalized and equitable WIL pedagogy. Adaptive learning pathways can specifically respond to individual strengths/weaknesses while AI mentor models provide real-time scaffolding contextually suited to each learner’s needs. These targeted applications promise renewed engagement and preparation for diverse student groups as they bridge learning with professional environments. Discussion delves into the principles and architecture guiding the responsible development of these AI tools, covering aspects from consent processes to impact audits. While empirical studies in the future will offer validation of efficacy, this paper contends for aligning the proposed approach with transformative learning goals. This research outlines innovative pathways that integrate equitable generative AI applications, laying the groundwork to inspire customized systems that respond to the call for reform in higher education. With a primary focus on prioritizing access and ethics, the creative utilization of technology has the potential to cultivate supportive structures that were previously considered untenable, benefiting students across the spectrum. The frameworks proposed here pave the way toward a future that is context-aware, fostering personalized pathways for work and learning.