Home > HCAS > HCAS_PUBS > HCAS_JOURNALS > TQR Home > TQR > Vol. 31 > No. 6 (2026)
Abstract
Because of the potential misuse of ChatGPT for coursework and student learning, marketing educators must investigate how to protect students' honesty and integrity. Additionally, it is crucial to examine whether marketing professors are modifying their pedagogy to promote learner autonomy rather than dependence on Artificial Intelligence (AI). This study addresses two central questions: How do marketing educators preserve students' honesty and integrity amidst the potential misuse of ChatGPT for coursework and learning? How are marketing professors adjusting their pedagogy to ensure learner autonomy rather than dependency on AI tools like ChatGPT? To explore these issues, semi-structured interviews were conducted with twenty-five professors from six countries, averaging 12.9 years of experience in teaching marketing courses. Results from thematic analysis revealed the benefits and drawbacks of AI-powered technology. The category with the highest number of mentions is Challenges and Concerns of AI in Education, with codes associated with concerns about ethical aspects, diminishing student skills, and the possible dependence that students may generate on these AI systems. In opposition, the category AI Entrance into Education reflected professors' openness to adopting Generative Artificial Intelligence (Gen AI) and awareness of the need to change teaching methods.
Keywords
ethical issues, generative artificial intelligence, higher academic integrity, marketing education, thematic analysis
Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 International License.
Recommended APA Citation
Shahid, M. N.,
Robayo-Pinzon, O.,
Rojas-Berrio, S.,
&
Irfan, S.
(2026).
Ethical Pedagogy in Marketing: Faculty Perspectives on Generative Artificial Intelligence Using Thematic Analysis.
The Qualitative Report,
31(6), 6115-6148.
DOI: https://doi.org/10.46743/1052-0147.7349
ORCID ID
0000-0001-7290-7531
