PriMera Scientific Engineering (ISSN: 2834-2550)

Review Article

Volume 4 Issue 5

Encouraging Trust in Ai-Powered Teaching Tools: Ranking Design Principles

Hani Alers* and Aleksandra Malinowska

April 26, 2024

Abstract

The advent of Artificial Intelligence (AI) in education promises transformative changes, but its effective adoption relies on the establishment of trust. Drawing from various scientific articles, this study identified seven design principles that influence students' trust towards AI-powered teaching tools. These principles are "Privacy", "Intelligence," "Fairness", "Controllability", "Engagement", "Transparency", and "Friendliness". A quantitative survey, involving students from the Hague University of Applied Sciences, was employed to rank these principles based on their perceived importance. The results revealed students find all principles important except for “friendliness”. Gender-based analysis indicated females' pronounced emphasis on "Fairness", "Friendliness" and “engagement” compared to males. Further analysis revealed students in ICT related fields value “Privacy” significantly more than their non-ICT related counterparts and “fairness” significantly less.

Keywords: Trust; Encourage trust; AI; Education; Design principle; Chatbot

References

  1. Alexandara Harry. Role of AI in Education (2023).
  2. S Jeong., et al. Editor’s overview: exploring mentoring relationships of various populations (2018).
  3. Luis Rolando Alarcón Llontop, Sindy Pasapera Ramírez and Karl Torres-Mirez. The ChatGPT Application: Initial Perceptions of University Teachers.
  4. Alexandara Harry. Role of AI in Education (2023).
  5. J Swayne. On the Use of Obstetric Instruments (1869).
  6. S Coghlan, T Miller and J Paterson. “Good Proctor or “Big Brother”? Ethics of Online Exam Supervision Technologies”. Philos Technol 34.4 (2021): 1581-1606.
  7. Hazem Zohny, J McMillan and M King. Ethics of generative AI (2023).
  8. Noor Irliana Mohd Rahim., et al. AI-Based Chatbots Adoption Model for Higher-Education Institutions: A Hybrid PLS-SEM-Neural Network Modelling Approach (2022).
  9. Libin. Integrated disciplines and future competencies: A blueprint for ethically aligned curriculum for IT, CS, ITC & beyond (2020).
  10. European Commission, Directorate-General for Education, Youth, Sport and Culture. Ethical guidelines on the use of artificial intelligence (AI) and data in teaching and learning for educators, Publications Office of the European Union (2022).
  11. U.S. Department of Education, Office of Educational Technology, Artificial Intelligence and Future of Teaching and Learning: Insights and Recommendations, Washington, DC (2023).
  12. Guo Y., et al. “Designing for trust: A set of design principles to increase trust in chatbot”. CCF Transactions on Pervasive Computing and Interaction 4.4 (2022): 474-481.
  13. IBM. Building trust in AI (2022). ibm.com: https://www.ibm.com/impact/ai-ethics
  14. Google. Responsibility: Our principles (2023). ai.google.com: https://ai.google/responsibility/principles/