AI Education in a Mirror: Challenges Faced by Academic and Industry Experts

• URL: http://arxiv.org/abs/2505.02856v1
• Date: Fri, 02 May 2025 16:52:49 GMT
• Title: AI Education in a Mirror: Challenges Faced by Academic and Industry Experts
• Authors: Mahir Akgun, Hadi Hosseini,
• Abstract summary: This study provides preliminary insights into challenges AI professionals encounter in both academia and industry.<n>We identify key challenges related to data quality and availability, model scalability, practical constraints, user behavior, and explainability.<n>These exploratory findings suggest that AI curricula could better integrate real-world complexities, software engineering principles, and interdisciplinary learning.
• Score: 15.332866859177747
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As Artificial Intelligence (AI) technologies continue to evolve, the gap between academic AI education and real-world industry challenges remains an important area of investigation. This study provides preliminary insights into challenges AI professionals encounter in both academia and industry, based on semi-structured interviews with 14 AI experts - eight from industry and six from academia. We identify key challenges related to data quality and availability, model scalability, practical constraints, user behavior, and explainability. While both groups experience data and model adaptation difficulties, industry professionals more frequently highlight deployment constraints, resource limitations, and external dependencies, whereas academics emphasize theoretical adaptation and standardization issues. These exploratory findings suggest that AI curricula could better integrate real-world complexities, software engineering principles, and interdisciplinary learning, while recognizing the broader educational goals of building foundational and ethical reasoning skills.

Related papers

• Whole-Person Education for AI Engineers [0.7895785395897614]
  The study identifies key motivations driving the call for change in AI engineering education.<n>The findings challenge the myths of technological neutrality and technosaviourism.<n>The study provides valuable insights and recommendations for transforming AI engineering education.
  arXiv  Detail & Related papers  (2025-06-10T19:03:31Z)
• Diversity and Inclusion in AI: Insights from a Survey of AI/ML Practitioners [4.761639988815896]
  Growing awareness of social biases and inequalities embedded in Artificial Intelligence (AI) systems has brought increased attention to the integration of Diversity and Inclusion (D&I) principles throughout the AI lifecycle.<n>Despite the rise of ethical AI guidelines, there is limited empirical evidence on how D&I is applied in real-world settings.<n>This study explores how AI and Machine Learning(ML) practitioners perceive and implement D&I principles and identifies organisational challenges that hinder their effective adoption.
  arXiv  Detail & Related papers  (2025-05-24T05:40:23Z)
• Bridging the Gap: Integrating Ethics and Environmental Sustainability in AI Research and Practice [57.94036023167952]
  We argue that the efforts aiming to study AI's ethical ramifications should be made in tandem with those evaluating its impacts on the environment.<n>We propose best practices to better integrate AI ethics and sustainability in AI research and practice.
  arXiv  Detail & Related papers  (2025-04-01T13:53:11Z)
• An Empirical Study on Decision-Making Aspects in Responsible Software Engineering for AI [5.564793925574796]
  This study investigates the ethical challenges and complexities inherent in responsible software engineering (RSE) for AI.<n>Personal values, emerging roles, and awareness of AIs societal impact influence responsible decision-making in RSE for AI.
  arXiv  Detail & Related papers  (2025-01-26T22:38:04Z)
• Evaluation of OpenAI o1: Opportunities and Challenges of AGI [100.85218639544654]
  o1-preview demonstrated remarkable capabilities, often achieving human-level or superior performance.<n>The model excelled in tasks requiring intricate reasoning and knowledge integration across various fields.<n>Overall results indicate significant progress towards artificial general intelligence.
  arXiv  Detail & Related papers  (2024-09-27T06:57:00Z)
• OlympicArena: Benchmarking Multi-discipline Cognitive Reasoning for Superintelligent AI [73.75520820608232]
  We introduce OlympicArena, which includes 11,163 bilingual problems across both text-only and interleaved text-image modalities.<n>These challenges encompass a wide range of disciplines spanning seven fields and 62 international Olympic competitions, rigorously examined for data leakage.<n>Our evaluations reveal that even advanced models like GPT-4o only achieve a 39.97% overall accuracy, illustrating current AI limitations in complex reasoning and multimodal integration.
  arXiv  Detail & Related papers  (2024-06-18T16:20:53Z)
• Bridging the Gap: A Study of AI-based Vulnerability Management between Industry and Academia [4.4037442949276455]
  Recent research advances in Artificial Intelligence (AI) have yielded promising results for automated software vulnerability management. The industry remains very cautious and selective about integrating AI-based techniques into their security vulnerability management workflow. We propose a set of future directions to help better understand industry expectations, improve the practical usability of AI-based security vulnerability research, and drive a synergistic relationship between industry and academia.
  arXiv  Detail & Related papers  (2024-05-03T19:00:50Z)
• Experts' View on Challenges and Needs for Fairness in Artificial Intelligence for Education [11.374344511408443]
  We conducted the first expert-driven systematic investigation on the challenges and needs for addressing fairness throughout the development of educational systems based on AI. We identified common and diverging views about the challenges and the needs faced by educational technologies experts in practice.
  arXiv  Detail & Related papers  (2022-06-23T13:29:39Z)
• 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)
• Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
  Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations. Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
  arXiv  Detail & Related papers  (2021-01-15T10:43:10Z)

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.