Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities
- URL: http://arxiv.org/abs/2509.08950v1
- Date: Wed, 10 Sep 2025 19:19:26 GMT
- Title: Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities
- Authors: Jarvis Haupt, Qin Lu, Yanning Shen, Jia Chen, Yue Dong, Dan McCreary, Mehmet Akçakaya, Georgios B. Giannakis,
- Abstract summary: Article explores the use of AI tools to facilitate and enhance education.<n> Primers are provided on several core technical issues that arise when using AI in educational settings.<n>The article serves as a resource for researchers and educators seeking to advance AI's role in engineering education.
- Score: 44.18936398140735
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- Perspectives and potential issues in using artificial intelligence for computer science education [0.0]
ChatGPT has ignited widespread interest in Large Language Models (LLMs) and broader Artificial Intelligence (AI) solutions.<n>While AI technologies hold potential for enhancing learning experiences, there are also emerging concerns.<n>These include the risk of over-reliance on technology, the potential erosion of fundamental cognitive skills, and the challenge of maintaining equitable access to such innovations.
arXiv Detail & Related papers (2025-09-17T06:34:23Z) - Understanding Student Acceptance, Trust, and Attitudes Toward AI-Generated Images for Educational Purposes [1.0878040851637998]
This study assesses students' acceptance, trust, and positive attitudes towards AI-generated images for educational tasks.
The results reveal high acceptance, trust, and positive attitudes among students who value the ease of use and potential academic benefits.
These findings suggest a need for developing comprehensive guidelines that address ethical considerations and intellectual property issues.
arXiv Detail & Related papers (2024-11-24T04:39:48Z) - Human-Centric eXplainable AI in Education [0.0]
This paper explores Human-Centric eXplainable AI (HCXAI) in the educational landscape.
It emphasizes its role in enhancing learning outcomes, fostering trust among users, and ensuring transparency in AI-driven tools.
It outlines comprehensive frameworks for developing HCXAI systems that prioritize user understanding and engagement.
arXiv Detail & Related papers (2024-10-18T14:02:47Z) - Artificial Intelligence from Idea to Implementation. How Can AI Reshape the Education Landscape? [0.0]
The paper shows how AI technologies have moved from theoretical constructs to practical tools that are reshaping pedagogical approaches and student engagement.
The essay concludes by discussing the prospects of AI in education, emphasizing the need for a balanced approach that considers both technological advancements and societal implications.
arXiv Detail & Related papers (2024-07-14T04:40:16Z) - Toward enriched Cognitive Learning with XAI [44.99833362998488]
We introduce an intelligent system (CL-XAI) for Cognitive Learning which is supported by artificial intelligence (AI) tools.
The use of CL-XAI is illustrated with a game-inspired virtual use case where learners tackle problems to enhance problem-solving skills.
arXiv Detail & Related papers (2023-12-19T16:13:47Z) - A new solution and concrete implementation steps for Artificial General Intelligence [0.0]
We propose a new approach to building a artificial general intelligence with self awareness.<n>In this article, we have put forward how to create genuine artificial general intelligence step by step.
arXiv Detail & Related papers (2023-08-12T13:31:02Z) - Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society.
A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests.
This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z) - An interdisciplinary conceptual study of Artificial Intelligence (AI)
for helping benefit-risk assessment practices: Towards a comprehensive
qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence.
The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z) - Pervasive AI for IoT Applications: Resource-efficient Distributed
Artificial Intelligence [45.076180487387575]
Artificial intelligence (AI) has witnessed a substantial breakthrough in a variety of Internet of Things (IoT) applications and services.
This is driven by the easier access to sensory data and the enormous scale of pervasive/ubiquitous devices that generate zettabytes (ZB) of real-time data streams.
The confluence of pervasive computing and artificial intelligence, Pervasive AI, expanded the role of ubiquitous IoT systems.
arXiv Detail & Related papers (2021-05-04T23:42:06Z) - Artificial Intelligence Technologies in Education: Benefits, Challenges
and Strategies of Implementation [8.54335661175611]
We have identified the benefits and challenges of implementing artificial intelligence in the education sector.
We have also reviewed modern AI technologies for learners and educators, currently available on the software market.
We have developed a strategy implementation model, described by a five-stage, generic process, along with the corresponding configuration guide.
arXiv Detail & Related papers (2021-02-11T11:09:41Z) - Empowering Things with Intelligence: A Survey of the Progress,
Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
We show how AI can empower the IoT to make it faster, smarter, greener, and safer.
First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving.
Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
arXiv Detail & Related papers (2020-11-17T13:14:28Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.