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As the semiconductor and photonics industries grapple with mounting business pressures, weaving resourceefficiency into engineering education has evolved from a priority to an imperative. Under the umbrella of FUTUR-IC, this paper highlights novel pedagogical strategies at Bridgewater State University (BSU) aimed at equipping photonics and optical engineers to address today’s ecological challenges. We detail two complementary approaches that together form a cohesive educational framework. The first involves a newly introduced fresh year-level seminar on Resource Efficient Microchip Manufacturing, which immerses students in resource-efficiency metrics such as Life Cycle Intelligence and “design for resourceefficiency” principles. By interlinking photonic integration concepts with tangible business impact assessments, this course fosters an early appreciation of how advanced technologies can be developed responsibly, with reduced energy consumption and minimized waste. The second approach redefines senior-level engineering design courses to embed multifaceted resourceefficiency criteria in the design process. Through project-based learning and collaboration with industry partners, students integrate photonic solutions with data-driven metrics, refining their ability to propose holistic prototypes. These initiatives go beyond technical mastery to cultivate interdisciplinary collaboration and critical thinking. This work illustrates how an integrated approach to engineering education can spark the next generation of practitioners to design for both technological excellence and business viability. 

Interactive learning environments facilitate learning by providing hints to fill the gaps in the understanding of a concept. Studies suggest that hints are not used optimally by learners. Either they are used unnecessarily or not used at all. It has been shown that learning outcomes can be improved by providing hints when needed. An effective hinttaking prediction model can be used by a learning environment to make adaptive decisions on whether to withhold or provide hints. Past work on student behavior modeling has focused extensively on the task of modeling a learner’s state of knowledge over time, referred to as knowledge tracing. The other aspects of a learner’s behavior such as tendency to use hints has garnered limited attention. Past knowledge tracing models either ignore the questions where a hint was taken or label hints taken as an incorrect response. We propose a multi-task memory-augmented deep learning model to jointly predict the hint-taking and the knowledge tracing task. The model incorporates the effect of past responses as well as hints taken on both the tasks. We apply the model on two datasets – ASSISTments 2009-10 skill builder dataset and Junyi Academy Math Practicing Log. The results show that deep learning models efficiently leverage the sequential information present in a learner’s responses. The proposed model significantly out-performs the past work on hint prediction by at least 12% points. Moreover, we demonstrate that jointly modeling the two tasks improves performance consistently across the tasks and the datasets, albeit by a small amount.