This paper explores the history of evolving teaching techniques in Digital Signal and Image Processing (DSIP) education with a focus on integrating Artificial Intelligence (AI) tools to close the continuing gap between theory and practice. Since DSIP has been at the center of Telecommunication, Medical Imaging, Robotics and AI, this paper examines active and student-centered learning paradigms, like collaborative, situation-based, and project-based learning (PBL) as viable pedagogical methods. The research methodology includes analyzing a survey using Data Visualization Tools in Python-3.12, 2023. This paper overviews the application of digital tools including MATLAB-R2024a, Python, Cloud-based systems, and AI-based learning analytics to promote experiential and adaptive learning to enable students to test complex signal and image processing systems. The findings emphasize the fact that these practices contribute to developing conceptual knowledge, critical thinking, and solving problems through engaging learners in real-life and data-driven scenarios. The results also indicate how the teachers can upgrade their instructional approach to technological innovations in teaching. Finally, this paper highlights the nature of AI-enriched pedagogies and practical experience to build the skills needed to operate in a more data-intensive, technologically advanced and sustainable engineering environment.
Mahajan et al. (Mon,) studied this question.
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