mlsanityData to 7 Oct 2026

Deploying AI for Signal Processing education: Selected challenges and intriguing opportunities

arXiv 2509.08950 ↗First posted 10 Sep 2025eess.SP cs.LGcited by 1

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 48 of its 96 references that are in the map. First posted on arXiv 10 Sep 2025.

Builds on

48 of 96 references.

References · 48

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2022ReAct: Synergizing Reasoning and Acting in Language Modelscited by 8,355
  3. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,383
  4. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  5. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,222
  6. 2022Survey of Hallucination in Natural Language Generationcited by 2,056
  7. 2016Equality of Opportunity in Supervised Learningcited by 2,000
  8. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
  9. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  10. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 911
  11. 2020On Faithfulness and Factuality in Abstractive Summarizationcited by 895
  12. 2017Counterfactual Fairnesscited by 802
36 more
  1. 2019GNNExplainer: Generating Explanations for Graph Neural Networkscited by 735
  2. 2023In-Context Retrieval-Augmented Language Modelscited by 694
  3. 2023Active Retrieval Augmented Generationcited by 506
  4. 2023LeanDojo: Theorem Proving with Retrieval-Augmented Language Modelscited by 414
  5. 2021Entity-Based Knowledge Conflicts in Question Answeringcited by 349
  6. 2023Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergycited by 343
  7. 2023Trusting Your Evidence: Hallucinate Less with Context-aware Decodingcited by 319
  8. 2022RARR: Researching and Revising What Language Models Say, Using Language Modelscited by 286
  9. 2013Bayesian Optimization in a Billion Dimensions via Random Embeddingscited by 178
  10. 2023Simple synthetic data reduces sycophancy in large language modelscited by 174
  11. 2022Rethinking with Retrieval: Faithful Large Language Model Inferencecited by 145
  12. 2021Hallucinated but Factual! Inspecting the Factuality of Hallucinations in Abstractive Summarizationcited by 128
  13. 2024Simulating Classroom Education with LLM-Empowered Agentscited by 125
  14. 2024Making Reasoning Matter: Measuring and Improving Faithfulness of Chain-of-Thought Reasoningcited by 108
  15. 2023SCOTT: Self-Consistent Chain-of-Thought Distillationcited by 103
  16. 2024Faithful Logical Reasoning via Symbolic Chain-of-Thoughtcited by 99
  17. 2020Controlled Hallucinations: Learning to Generate Faithfully from Noisy Datacited by 83
  18. 2024LLMs Will Always Hallucinate, and We Need to Live With Thiscited by 77
  19. 2023In-context Pretraining: Language Modeling Beyond Document Boundariescited by 76
  20. 2023MindMap: Knowledge Graph Prompting Sparks Graph of Thoughts in Large Language Modelscited by 73
  21. 2023InstructZero: Efficient Instruction Optimization for Black-Box Large Language Modelscited by 68
  22. 2023The Challenge of Using LLMs to Simulate Human Behavior: A Causal Inference Perspectivecited by 57
  23. 2024Prompt Optimization with Human Feedbackcited by 35
  24. 20196G Wireless Communication Systems: Applications, Requirements, Technologies, Challenges, and Research Directionscited by 34
  25. 2023Chain of Natural Language Inference for Reducing Large Language Model Ungrounded Hallucinationscited by 34
  26. 2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
  27. 2024Large Language Models for Education: A Surveycited by 32
  28. 2024Causal Inference with Large Language Model: A Surveycited by 31
  29. 2023Use Your INSTINCT: INSTruction optimization for LLMs usIng Neural bandits Coupled with Transformerscited by 29
  30. 2023BatGPT: A Bidirectional Autoregessive Talker from Generative Pre-trained Transformercited by 23
  31. 2022Surrogate modeling for Bayesian optimization beyond a single Gaussian processcited by 22
  32. 2023Generative Pretrained Autoregressive Transformer Graph Neural Network applied to the Analysis and Discovery of Novel Proteinscited by 10
  33. 2024Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Taskscited by 10
  34. 2016Kernel-Based Structural Equation Models for Topology Identification of Directed Networkscited by 8
  35. 2023Fairness-Aware Graph Filter Designcited by 8
  36. 2024FairWire: Fair Graph Generationcited by 6

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Low-Rank Adaptation Redux for Large Modelscited by 2