Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866918139025948672 |
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| author | Haupt, Jarvis Lu, Qin Shen, Yanning Chen, Jia Dong, Yue McCreary, Dan Akçakaya, Mehmet Giannakis, Georgios B. |
| author_facet | Haupt, Jarvis Lu, Qin Shen, Yanning Chen, Jia Dong, Yue McCreary, Dan Akçakaya, Mehmet Giannakis, Georgios B. |
| contents | Powerful artificial intelligence (AI) tools that have emerged in recent years -- including large language models, automated coding assistants, and advanced image and speech generation technologies -- are the result of monumental human achievements. These breakthroughs reflect mastery across multiple technical disciplines and the resolution of significant technological challenges. However, some of the most profound challenges may still lie ahead. These challenges are not purely technical but pertain to the fair and responsible use of AI in ways that genuinely improve the global human condition. This article explores one promising application aligned with that vision: the use of AI tools to facilitate and enhance education, with a specific focus on signal processing (SP). It presents two interrelated perspectives: identifying and addressing technical limitations, and applying AI tools in practice to improve educational experiences. Primers are provided on several core technical issues that arise when using AI in educational settings, including how to ensure fairness and inclusivity, handle hallucinated outputs, and achieve efficient use of resources. These and other considerations -- such as transparency, explainability, and trustworthiness -- are illustrated through the development of an immersive, structured, and reliable "smart textbook." The article serves as a resource for researchers and educators seeking to advance AI's role in engineering education. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_08950 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities Haupt, Jarvis Lu, Qin Shen, Yanning Chen, Jia Dong, Yue McCreary, Dan Akçakaya, Mehmet Giannakis, Georgios B. Signal Processing Machine Learning Powerful artificial intelligence (AI) tools that have emerged in recent years -- including large language models, automated coding assistants, and advanced image and speech generation technologies -- are the result of monumental human achievements. These breakthroughs reflect mastery across multiple technical disciplines and the resolution of significant technological challenges. However, some of the most profound challenges may still lie ahead. These challenges are not purely technical but pertain to the fair and responsible use of AI in ways that genuinely improve the global human condition. This article explores one promising application aligned with that vision: the use of AI tools to facilitate and enhance education, with a specific focus on signal processing (SP). It presents two interrelated perspectives: identifying and addressing technical limitations, and applying AI tools in practice to improve educational experiences. Primers are provided on several core technical issues that arise when using AI in educational settings, including how to ensure fairness and inclusivity, handle hallucinated outputs, and achieve efficient use of resources. These and other considerations -- such as transparency, explainability, and trustworthiness -- are illustrated through the development of an immersive, structured, and reliable "smart textbook." The article serves as a resource for researchers and educators seeking to advance AI's role in engineering education. |
| title | Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2509.08950 |