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Artificial Intelligence In Helping People With Disabilities: Opportunities And Challenges

Author

Listed:
  • Radka Nacheva

    (University of Economics - Varna, Bulgaria)

  • Maciej Czaplewski

    (Institute of Spatial Management and Socio-Economic Geography, University of Szczecin, Poland)

Abstract

Artificial intelligence (AI) is revolutionizing teaching, learning, and administrative processes in higher education. AI-driven personalized learning platforms, virtual tutors, content creation tools, chatbots, and adaptive learning platforms offer tailored educational experiences, fostering student engagement and autonomy. These tools promote active learning, enhance instructional content, and provide round-the-clock assistance. However, the integration raises ethical concerns like data privacy, algorithmic bias, and the displacement of traditional teaching roles. Therefore, ethical guidelines and regulatory frameworks are crucial for responsible AI implementation in higher education settings. The application of AI holds the potential to change the teaching and learning landscape, foster innovation, and create a more inclusive and personalized educational experience. In this regard, the purpose of this paper is to analyse commonly used AI-powered tools in higher education which could be used to better the digital accessibility for people with disabilities. The objectives of this paper are related to the study of the features of Intelligent Tutoring Systems and AI-powered virtual tutors, as well as AI-driven chatbots and virtual assistants; to conduct a comparative analysis of AI chatbots to track the differences in their features that are important to better the accessibility. The research hypothesizes that texts generated with AI-powered tools need to improve readability. The accessibility or, more specifically, the readability of generated texts was checked with the OpenAI ChatGPT and Microsoft Copilot chatbots. Results are compared based on key readability metrics.

Suggested Citation

  • Radka Nacheva & Maciej Czaplewski, 2024. "Artificial Intelligence In Helping People With Disabilities: Opportunities And Challenges," HR and Technologies, Creative Space Association, issue 1, pages 102-124.
    • Handle: RePEc:arb:journl:y:2024:i:1:p:102-124
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    References listed on IDEAS

    as
    1. Choi, Jay Pil & Jeon, Doh-Shin & Kim, Byung-Cheol, 2019. "Privacy and personal data collection with information externalities," Journal of Public Economics, Elsevier, vol. 173(C), pages 113-124.
    2. Liliya Mileva & Pavel Petrov & Plamen Yankov & Julian Vasilev & Stefka Petrova, 2021. "Prototype model for big data predictive analysis in logistics area with Apache Kudu," Economics and computer science, Publishing house "Knowledge and business" Varna, issue 1, pages 20-41.
    3. Dwivedi, Yogesh K. & Hughes, Laurie & Ismagilova, Elvira & Aarts, Gert & Coombs, Crispin & Crick, Tom & Duan, Yanqing & Dwivedi, Rohita & Edwards, John & Eirug, Aled & Galanos, Vassilis & Ilavarasan, , 2021. "Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy," International Journal of Information Management, Elsevier, vol. 57(C).
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Agnieszka Budziewicz-Guźlecka & Katya Antonova, 2024. "Digitalization as an Element of Development and Challenges in the Tourism Sector," HR and Technologies, Creative Space Association, issue 2, pages 17-31.

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    JEL classification:

    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

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