MARATTO

article · Journal of Applied Learning & Teaching

Generative Artificial Intelligence in distance education: Transformations, challenges, and impact on academic integrity and student voice

202440 citationsOpen accessUniversity of South Africa

In plain language

Generative artificial intelligence is reshaping distance education and encouraging a transition towards student-centred approaches for responsible technology use. By applying the technology acceptance model, qualitative research was undertaken at a South African open distance and e-learning university to evaluate how this technology affects academic integrity and the development of students' own voices. Data were gathered through interviews with lecturers, open-ended evaluations with administrative personnel, and focus groups with first-year academic writing students. The results identify a critical tension between concerns regarding academic dishonesty and optimism about artificial intelligence acting as a tool to bolster learning confidence. Addressing this divide requires institutional strategies that successfully balance academic standards with student empowerment, providing insights into the broader pedagogical potential and operational challenges within distance learning environments.

Key takeaways

  • Generative artificial intelligence prompts distance education institutions to adopt student-centred initiatives for responsible adoption.
  • A notable gap exists between negative perceptions of artificial intelligence regarding academic integrity and positive views of its ability to boost student confidence.
  • First-year writing students, lecturers, and administrative staff demonstrate differing viewpoints on the utility and risks of artificial intelligence tools.
  • Institutions require clear strategies to preserve authentic student voices while maintaining academic standards.

Why it matters

As universities expand online offerings, generative artificial intelligence alters how coursework is produced and assessed. Understanding stakeholder perspectives helps distance-learning institutions address genuine risks of academic misconduct while simultaneously harnessing automated tools to improve learner self-assurance, writing competence, and student engagement in remote higher education environments.

Commercialisation angle

The abstract does not indicate an application pathway, presenting early-stage qualitative institutional research rather than a commercial product or technical tool.

AI-generated from the published abstract. Always read the original work before citing.

Abstract

Generative artificial intelligence (GenAI) has reshaped distance education by prompting a shift towards student-centred initiatives to promote responsible AI usage. This study explores the transformative impact of GenAI in distance learning and focuses on academic integrity and student voices. This study uses the technology acceptance model to investigate how GenAI influences distance education. Three objectives guide the study: (1) exploring the transformative effects of GenAI in distance education, (2) understanding its impact on academic integrity, and (3) examining its influence on students’ academic voices in a South African open distance and e-learning university. Qualitative data was gathered through interviews with lecturers, open-ended evaluation questions with administrative staff, and focus group discussions with first-year students in an academic writing module. Findings highlight the need to bridge the gap between negative perceptions of AI’s impact on academic integrity and positive views on its potential to boost student confidence in learning. This research study aims to analyse GenAI’s role in distance education and provide insight into its potential, challenges, and strategies to ensure academic integrity and preserve students’ voices.

Research topics

  • Artificial Intelligence in Healthcare and Education
  • Online Learning and Analytics

Sustainable Development Goals

Read the original research

This page summarises published work. The authoritative version sits with the publisher.

DOI: 10.37074/jalt.2024.7.1.41

Is something wrong with this record? Report it or request removal.

Discussion

Discuss this research

Have you built on this work, tried to replicate it, or seen it applied in practice? Share what you know. Verified researchers and MARATTO™ domain experts can open a discussion, and any member can reply. Contributions are reviewed before they appear.

No discussion yet. Open the first thread.