IDEAS home Printed from https://ideas.repec.org/a/arb/journl/y2024i1p102-124.html

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
    as

    Download full text from publisher

    File URL: https://journal.cspace-ngo.com/arb/Issues/2024/1/6-Nacheva-Czaplewski-2024.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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).
    2. 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.
    3. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Goonj Mohan, 2024. "The Data Economy and Polarization on Social Media," UB School of Economics Working Papers 2024/462, University of Barcelona School of Economics.
    2. Ghosh, Vinit & Chotia, Varun & Alghafes, Rsha & Basahel, Sarah & Nazrul, Asif, 2025. "Bridging AI minds and corporate sustainability - Exploring the role of business model innovation and organizational sensemaking," Technological Forecasting and Social Change, Elsevier, vol. 218(C).
    3. Sundberg, Leif & Holmström, Jonny, 2023. "Democratizing artificial intelligence: How no-code AI can leverage machine learning operations," Business Horizons, Elsevier, vol. 66(6), pages 777-788.
    4. Akter, Shahriar & Dwivedi, Yogesh K. & Sajib, Shahriar & Biswas, Kumar & Bandara, Ruwan J. & Michael, Katina, 2022. "Algorithmic bias in machine learning-based marketing models," Journal of Business Research, Elsevier, vol. 144(C), pages 201-216.
    5. Jorge Padilla, 2020. "Big Tech “banks”, financial stability and regulation," Revista de Estabilidad Financiera, Banco de España, issue Spring.
    6. Zhaoli Li & Yifan Xu, 2026. "Information sharing under consumer privacy protection in an online marketplace," Annals of Operations Research, Springer, vol. 356(2), pages 985-1020, January.
    7. Blüher, Georg & Schleth, Henrik C. & Kanbach, Dominik K. & Kallmuenzer, Andreas & Kraus, Sascha, 2026. "Human touch in the age of AI: Customer willingness to adopt AI-enabled hospitality services," Technology in Society, Elsevier, vol. 84(C).
    8. Eilat, Ran & Eliaz, Kfir & Mu, Xiaosheng, 2021. "Bayesian privacy," Theoretical Economics, Econometric Society, vol. 16(4), November.
    9. Strausz Roland, 2026. "Consumer Consent Regulation," German Economic Review, De Gruyter, vol. 27(1), pages 37-51.
    10. Sergio Mariotti, 2022. "The economics–engineering nexus: response to the commentaries," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 49(1), pages 1-29, March.
    11. Bennich, Amelie, 2024. "The digital imperative: Institutional pressures to digitalise," Technology in Society, Elsevier, vol. 76(C).
    12. Li, Jianpei & Zhang, Wanzhu, 2025. "The Value of Anonymous Option," MPRA Paper 124009, University Library of Munich, Germany.
    13. Natvik, Gisle J. & Tangerås, Thomas, 2023. "Paying with Personal Data," Working Paper Series 1481, Research Institute of Industrial Economics.
    14. Michael Weber & Martin Engert & Norman Schaffer & Jörg Weking & Helmut Krcmar, 2023. "Organizational Capabilities for AI Implementation—Coping with Inscrutability and Data Dependency in AI," Information Systems Frontiers, Springer, vol. 25(4), pages 1549-1569, August.
    15. Zahlan, Ahmed, 2025. "Strategic resource mobilization for AI entrepreneurship in healthcare: Qualitative insights from startup founders," Technovation, Elsevier, vol. 145(C).
    16. Jorge Padilla, 2020. "Big Tech “banks”, financial stability and regulation," Revista de Estabilidad Financiera, Banco de España, issue Primavera.
    17. Abou-Foul, Mohamad & Ruiz-Alba, Jose L. & López-Tenorio, Pablo J., 2023. "The impact of artificial intelligence capabilities on servitization: The moderating role of absorptive capacity-A dynamic capabilities perspective," Journal of Business Research, Elsevier, vol. 157(C).
    18. María Fernández-Fernández & Álvaro Hernández-Tamurejo & Paula González-Padilla, 2026. "The AI trust factor: an MCA analysis of automation and decision-making in entrepreneurship," International Entrepreneurship and Management Journal, Springer, vol. 22(1), pages 1-39, March.
    19. Flavio Pino, 2022. "The microeconomics of data – a survey," Economia e Politica Industriale: Journal of Industrial and Business Economics, Springer;Associazione Amici di Economia e Politica Industriale, vol. 49(3), pages 635-665, September.
    20. Dirk Bergemann & Alessandro Bonatti, 2024. "Data, Competition, and Digital Platforms," American Economic Review, American Economic Association, vol. 114(8), pages 2553-2595, August.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arb:journl:y:2024:i:1:p:102-124. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Pavlina Ivanova (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.