<p>The ability to innovate and be an entrepreneur is a skill that should be taught to college students who must learn to adapt, innovate and solve problems in a rapidly changing economic system. A reliable prediction and right assessment of these competencies is a must for customized education and entrepreneurship guidance. The majority of present methods of measuring entrepreneurial potential are, however, static and are measured by means of expert opinions or performance indicators which fail to capture the dynamic and multidimensional nature of entrepreneurial potential. This restriction results in poor adaptability, subjectivity and poor prediction of the data. This paper introduces a deep reinforcement learning (DRL) based framework named Innovation-Evaluation DRL Predictor (IEDP) to solve the above challenges and evaluate and predict students capability of innovation and entrepreneurship. For IEDP, the outcomes of creativity are quantified through an IEDP reward function, which combines expected and actual reward functions based on items such as originality deviation from baseline responses, novelty score based on behavioural divergence in decision patterns, and performance gain over iterative learning episodes. The approach incentivizes creativity-oriented results, enhances decision-making strategies, and forecasts the likelihood of entrepreneurial success with greater accuracy. The proposed method can be used for continuous monitoring of student performance, adaptive feedback and directing allocation of resources for innovative education by educators, institutions and policy makers. IEDP has been shown to outperform traditional assessment approaches in terms of predictive power, adaptability to changing surroundings, and correlation with entrepreneurial outcomes in the real world. The study highlights the potential of AI-assisted approaches in developing innovative, future-ready employees.</p>