Evolving Pedagogy in Digital Signal Processing Education: AI-Assisted Review and Analysis
Bordallo López, Miguel (2025-03-10)
IEEE
10.03.2025
M. Bordallo López, "Evolving Pedagogy in Digital Signal Processing Education: AI-Assisted Review and Analysis," in IEEE Access, vol. 13, pp. 45559-45567, 2025, doi: 10.1109/ACCESS.2025.3549477
https://creativecommons.org/licenses/by/4.0/
© 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
https://creativecommons.org/licenses/by/4.0/
© 2025 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
https://creativecommons.org/licenses/by/4.0/
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202503192109
https://urn.fi/URN:NBN:fi:oulu-202503192109
Tiivistelmä
Abstract
This survey provides a comprehensive review of the evolving pedagogical approaches in Digital Signal Processing (DSP) education. By employing an AI-assisted recursive search methodology, the paper systematically examines traditional and innovative teaching methods, ranging from classic lecture styles to modern, technology-enhanced, and interactive strategies. The focus is on evaluating the effectiveness of these methodologies in teaching complex DSP concepts and fostering practical skills. The integration of digital tools, online learning platforms, and laboratory experiences is critically analyzed, emphasizing their role in enhancing student engagement and comprehension. Through comparative analysis and insights derived from AI-assisted synthesis, this paper offers actionable recommendations for educators to optimize DSP teaching techniques in the rapidly advancing field of engineering education.
This survey provides a comprehensive review of the evolving pedagogical approaches in Digital Signal Processing (DSP) education. By employing an AI-assisted recursive search methodology, the paper systematically examines traditional and innovative teaching methods, ranging from classic lecture styles to modern, technology-enhanced, and interactive strategies. The focus is on evaluating the effectiveness of these methodologies in teaching complex DSP concepts and fostering practical skills. The integration of digital tools, online learning platforms, and laboratory experiences is critically analyzed, emphasizing their role in enhancing student engagement and comprehension. Through comparative analysis and insights derived from AI-assisted synthesis, this paper offers actionable recommendations for educators to optimize DSP teaching techniques in the rapidly advancing field of engineering education.
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