Feedback is a cornerstone of academic development, particularly in marketing education, where skill development often requires iteration and applied practice. While instructors spend considerable time providing effective feedback, artificial intelligence (AI) tools can significantly reduce this burden by generating timely, tailored responses. For example, AI-based chatbots can supplement instruction, providing students with timely feedback and reducing instructors’ workload. This study examines how marketing students perceive feedback quality based on its source, specifically whether it is delivered by an AI system or a human instructor. Through a scenario-based experiment, we investigate student perceptions of feedback quality and effectiveness based on the source (AI vs. Instructor) or valence (positive vs. negative). Our findings provide guidance for professors who want to incorporate AI tools into their teaching practices. Understanding student perceptions of feedback source is crucial for marketing educators who want to leverage AI systems to enhance the educational experience without compromising the human component that students value.
AckermanD. S.DommeyerC. J.GrossB. L. (2017). The effects of source, revision possibility, and amount of feedback on marketing students’ impressions of feedback on an assignment. Journal of Marketing Education, 39(1), 17–29. https://doi.org/10.1177/0273475316628293
2.
AikenL. S.WestS. G. (1991). Multiple regression: Testing and interpreting interactions. Sage.
3.
AlqahtaniT.BadreldinH. A.AlrashedM.AlshayaA. I.AlghamdiS. S.bin SalehK.AlowaisS. A.AlshayaO. A.RahmanI.Al YamiM. S.AlbekairyA. M. (2023). The emergent role of artificial intelligence, natural language processing, and large language models in higher education and research. Research in Social and Administrative Pharmacy, 19(8), 1236–1242. https://doi.org/10.1016/j.sapharm.2023.05.016
4.
AshwinP.McVittyD. (2015). The meanings of student engagement: Implications for policies and practices. In CurajA.MateiL.PricopieR.SalmiJ.ScottP. (Eds.), The European higher education area: Between critical reflections and future policies (pp. 343–359). Springer.
5.
BackhausK.LiffJ. P. (2007). Cognitive styles and approaches to studying in management education. Journal of Management Education, 31(4), 445–466. https://doi.org/10.1177/1052562905284674
6.
BerezanO.KrishenA. S.GarceraS. (2023). Back to the basics: Handwritten journaling, student engagement, and bloom’s learning outcomes. Journal of Marketing Education, 45(1), 5–17. https://doi.org/10.1177/02734753221075557
7.
BerloD. K.LemertJ. B.MertzR. J. (1969). Dimensions for evaluating the acceptability of message sources. Public Opinion Quarterly, 33(4), 563–576. https://doi.org/10.1086/267745
8.
BlutM.WangC.WünderlichN. V.BrockC. (2021). Understanding anthropomorphism in service provision: A meta-analysis of physical robots, chatbots, and other AI. Journal of the Academy of Marketing Science, 49(4), 632–658. https://doi.org/10.1007/s11747-020-00762-y
9.
BrynjolfssonE.LiD.RaymondL. R. (2023). Generative AI at work (NBER Working Paper No.31161). National Bureau of Economic Research. https://doi.org/10.3386/w31161
10.
ButlerD. L.WinneP. H. (1995). Feedback and self-regulated learning: A theoretical synthesis. Review of Educational Research, 65(3), 245–281.
11.
Çağlar-ÖzhanŞ.TekeliP.Arkün-KocadereS. (2025). Comparison of AI-generated and instructor feedback: No significant difference in perceived feedback quality and neither on performance. Journal of Computer Assisted Learning, 41(5), Article e70134. https://doi.org/10.1111/jcal.70134
12.
CarlessD. (2012). Trust and its role in facilitating dialogic feedback. In BoudD.MolloyE. (Eds.), Feedback in higher and professional education: Understanding it and doing it well (pp. 90–103). Routledge.
13.
CleggS.SarkarS. (2024). Artificial intelligence and management education: A conceptualization of human-machine interaction. The International Journal of Management Education, 22(3), Article 101007. https://doi.org/10.1016/j.ijme.2024.101007
14.
CowanM.FoxG.LarsonK. (2025). Can AI level the playing field? How AI-assisted assessment impacts gender bias in student evaluations of marketing instructors. Journal of Marketing Education, 47(2), 126–137. https://doi.org/10.1177/02734753241303742
15.
DavisS. E.DarguschJ. M. (2015). Feedback, iterative processing and academic trust-teacher education students’ perceptions of assessment feedback. Australian Journal of Teacher Education, 40(1), 177–191.
16.
de WynterA.WangX.SokolovA.GuQ.ChenS.-Q. (2023). An evaluation on large language model outputs: Discourse and memorization. Natural Language Processing Journal, 4, 1–12. https://doi.org/10.1016/j.nlp.2023.100024
17.
ErE.AkçapınarG.BayazıtA.NorooziO.BanihashemS. K. (2025). Assessing student perceptions and use of instructor versus AI-generated feedback. British Journal of Educational Technology, 56(3), 1074–1091. https://doi.org/10.1111/bjet.13558
18.
EvansC.WaringM. (2011). Student teacher assessment feedback preferences: The influence of cognitive styles and gender. Learning and Individual Differences, 21(3), 271–280. https://doi.org/10.1016/j.lindif.2010.11.011
19.
GiffinK. (1967). The contribution of studies of source credibility to a theory of interpersonal trust in the communication process. Psychological Bulletin, 68(2), 104–120. https://doi.org/10.1037/h0024833
GrewalD.GuhaA.Beccacece SatorninoC.BeckerM. (2025). The future of marketing and marketing education. Journal of Marketing Education, 47(1), 61–77. https://doi.org/10.1177/02734753241269838
22.
GuhaA.GrewalD.AtlasS. (2024). Generative AI and marketing education: What the future holds. Journal of Marketing Education, 46(1), 6–17. https://doi.org/10.1177/02734753231215436
23.
HamdaniM. R.WallinA.AshkanasyN. M.Fenton-O’CreevyM. (2023). Common methodological issues in quantitative management education research and recommendations for authors. Journal of Management Education, 47(6), 572–588. https://doi.org/10.1177/10525629231186158
24.
HasijaA.EsperT. L. (2022). In artificial intelligence (AI) we trust: A qualitative investigation of AI technology acceptance. Journal of Business Logistics, 43(3), 388–412. https://doi.org/10.1111/jbl.12301
HauserD. J.SchwarzN. (2016). Attentive Turkers: MTurk participants perform better on online attention checks than do subject pool participants. Behavior Research Methods, 48(1), 400–407.
27.
HayesA. F. (2017). Introduction to mediation, moderation, and conditional process analysis: A regression-based approach (2nd ed.). Guilford Press.
28.
HendersonM.BearmanM.ChungJ.FawnsT.Buckingham ShumS.MatthewsK. E.de Mello HerediaJ. (2025). Comparing Generative AI and teacher feedback: student perceptions of usefulness and trustworthiness. Assessment & Evaluation in Higher Education, 1–16. https://doi.org/10.1080/02602938.2025.2502582.
HovlandC. I.JanisI. L.KelleyH. H. (1953). Communication and persuasion. Yale University Press.
31.
HovlandC. I.WeissW. (1951). The influence of source credibility on communication effectiveness. Public Opinion Quarterly, 15(4), 635–650. https://doi.org/10.1086/266350
32.
HydeS. J.BusbyA.BonnerR. L. (2024). Tools or fools: Are we educating managers or creating tool-dependent robots?Journal of Management Education, 48(4), 708–734. https://doi.org/10.1177/10525629241230357
33.
IlgenD. R.FisherC. D.TaylorM. S. (1979). Consequences of individual feedback on behavior in organizations. Journal of Applied Psychology, 64(4), 349–371. https://doi.org/10.1037/0021-9010.64.4.349
34.
JinJ.WalkerJ.ReczekR. W. (2024). Avoiding embarrassment online: Response to and inferences about chatbots when purchases activate self-presentation concerns. Journal of Consumer Psychology, 34(2), 185–202. https://doi.org/10.1002/jcpy.1414
35.
KhanA. W.MishraA. (2024). AI credibility and consumer-AI experiences: A conceptual framework. Journal of Service Theory and Practice, 34(1), 66–97.
36.
KlugerA. N.DeNisiA. (1996). The effects of feedback interventions on performance: A historical review, a meta-analysis, and a preliminary feedback intervention theory. Psychological Bulletin, 119(2), 254–284. https://doi.org/10.1037/0033-2909.119.2.254
37.
KutnerM. H.NachtsheimC. J.NeterJ.LiW. (2005). Applied linear statistical models (5th ed.). The McGraw-Hill Companies.
38.
LangerM.KönigC. J.BackC.HemsingV. (2023). Trust in artificial intelligence: Comparing trust processes between human and automated trustees in light of unfair bias. Journal of Business and Psychology, 38(3), 493–508. https://doi.org/10.1007/s10869-022-09829-9
39.
LazarusR. S. (1991). Emotion and adaptation. Oxford University Press.
40.
LechermeierJ.FassnachtM. (2018). How do performance feedback characteristics influence recipients’ reactions? A state-of-the-art review on feedback source, timing, and valence effects. Management Review Quarterly, 68(2), 145–193. https://doi.org/10.1007/s11301-018-0136-8
41.
LimS.SchmälzleR. (2024). The effect of source disclosure on evaluation of AI-generated messages. Computers in Human Behavior: Artificial Humans, 2(1), Article 100058. https://doi.org/10.1016/j.chbah.2024.100058
42.
LipnevichA. A.GjicaliK.AsilM.SmithJ. K. (2021). Development of a measure of receptivity to instructional feedback and examination of its links to personality. Personality and Individual Differences, 169, 110086. https://doi.org/10.1016/j.paid.2020.110086
43.
LipnevichA. A.Lopera-OquendoC. (2024). Receptivity to instructional feedback: A validation study in the secondary school context in Singapore. European Journal of Psychological Assessment, 40(1), 22–32. https://doi.org/10.1027/1015-5759/a000733
44.
LipnevichA. A.Lopera-OquendoC.ParkM. J. (2023). Receptivity to instructional feedback. In LipnevichA. A.ToJ.TanK. H. K. (Eds.), Unpacking students’ engagement with feedback: Pedagogy and partnership in practice (pp. 41–56). Routledge.
45.
LipnevichA. A.PreckelF.KrummS. (2016). Mathematics attitudes and their unique contribution to achievement: Going over and above cognitive ability and personality. Learning and Individual Differences, 47, 70–79. https://doi.org/10.1016/j.lindif.2015.12.027
46.
LipnevichA. A.SmithJ. K. (Eds.). (2018). The Cambridge handbook of instructional feedback. Cambridge University Press.
47.
LiuS.XiaoY.WangX. (2022). How does feedback valence improve team creativity by influencing team relationship conflict?Psychology Research and Behavior Management, 15, 2391–2407. https://doi.org/10.2147/PRBM.S373350
48.
LongoniC.BonezziA.MorewedgeC. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629–650. https://doi.org/10.1093/jcr/ucz013
49.
LuQ.YaoY.XiaoL.YuanM.WangJ.ZhuX. (2024). Can ChatGPT effectively complement teacher assessment of undergraduate students’ academic writing?Assessment and Evaluation in Higher Education, 49(5), 616–633. https://doi.org/10.1080/02602938.2024.2301722
50.
McAllisterD. J. (1995). Affect-and cognition-based trust as foundations for interpersonal cooperation in organizations. Academy of Management Journal, 38(1), 24–59.
51.
McCroskeyJ. C.TevenJ. J. (1999). Goodwill: A reexamination of the construct and its measurement. Communication Monographs, 66(1), 90–103. https://doi.org/10.1080/03637759909376464
52.
MoffittR. L.PadgettC.GrieveR. (2020). Accessibility and emotionality of online assessment feedback: Using emoticons to enhance student perceptions of marker competence and warmth. Computers & Education, 143, 1–22. https://doi.org/10.1016/j.compedu.2019.103654
53.
NaminA.DargahiR.RohmA. J. (2024). The role of feedback source and valence in crowdsourced idea innovation. Behaviour & Information Technology, 43(3), 458–474. https://doi.org/10.1080/0144929X.2023.2177820
54.
NarcissS. (2013). Designing and evaluating tutoring feedback strategies for digital learning environments on the basis of the interactive tutoring feedback model. Digital Education Review, 23, 7–26.
55.
NazaretskyT.Mejia-DomenzainP.SwamyV.FrejJ.KäserT. (2026). Who gives feedback matters: Student biases towards human and AI-generated formative feedback. Journal of Computer Assisted Learning, 42(1), Article e70153. https://doi.org/10.1111/jcal.70153
56.
NicolD. J.Macfarlane-DickD. (2006). Formative assessment and self-regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199–218. https://doi.org/10.1080/03075070600572090
57.
NoyS.ZhangW. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. https://ssrn.com/abstract=4375283
58.
NygrenT.SamuelssonM.HanssonP.-O.EfimovaE.BachelderS. (2025). AI versus human feedback in mixed reality simulations: Comparing LLM and expert mentoring in preservice teacher education on controversial issues. International Journal of Artificial Intelligence in Education, 35(5), 2856–2888. https://doi.org/10.1007/s40593-025-00484-8
59.
OhanianR. (1990). Construction and validation of a scale to measure celebrity endorsers’ perceived expertise, trustworthiness, and attractiveness. Journal of Advertising, 19(3), 39–52. https://doi.org/10.1080/00913367.1990.10673191
60.
OppenheimerD. M.MeyvisT.DavidenkoN. (2009). Instructional manipulation checks: Detecting satisficing to increase statistical power. Journal of Experimental Social Psychology, 45(4), 867–872.
61.
PerdueB. C.SummerJ. O. (1986). Checking the success of manipulations in marketing experiments. Journal of Marketing Research, 23(4), 317–326. https://doi.org/10.1177/002224378602300401
62.
PreacherK. P.HayesA. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891.
63.
PrilopC. N.WeberK. E.PrinsF. J.KleinknechtM. (2021). Connecting feedback to self-efficacy: Receiving and providing peer feedback in teacher education. Studies in Educational Evaluation, 70, Article 101062. https://doi.org/10.1016/j.stueduc.2021.101062
64.
RogersP. P.AllenC.BusbyA. (2025). Marketing educators and artificial intelligence: A perspective on productivity and innovation. Journal of Marketing Education, 47(2), 156–169. https://doi.org/10.1177/02734753241299095
65.
RosemanI. J.SmithC. A. (2001). Appraisal theory. In SchererK. R.SchorrA.JohnstoneT. (Eds.), Appraisal processes in emotion: Theory, methods, research (pp. 3–19). Oxford University Press.
66.
RosseelY. (2012). lavaan: An R Package for structural equation modeling. Journal of Statistical Software, 48(2), 1–36.
67.
RuweT.KuklickL. (2025). Quality counts? Examining the role of feedback provider and feedback quality on students’ feedback perceptions. British Journal of Educational Technology, 57(1), 272–298. https://doi.org/10.1111/bjet.70011
68.
RyanM. (2020). In AI we trust: Ethics, artificial intelligence, and reliability. Science and Engineering Ethics, 26(5), 2749–2767. https://doi.org/10.1007/s11948-020-00228-y
69.
SadlerD. R. (1989). Formative assessment and the design of instructional systems. Instructional Science, 18(2), 119–144. https://doi.org/10.1007/BF00117714
70.
SchlegelmilchB. B.MillsA. J. (2025). Artificial intelligence and the future of marketing education. Journal of Marketing Education, 47(2), 91–93. https://doi.org/10.1177/02734753251336956
SimpsonE. K.KahlerR. C. (1981). A scale for source credibility; Validated in the selling context. The Journal of Personal Selling & Sales Management, 1(1), 17–25. https://doi.org/10.1080/08853134.1981.10754191
73.
SteissJ.TateT.GrahamS.CruzJ.HebertM.WangJ.MoonY.TsengW.WarschauerM.OlsonC. B. (2024). Comparing the quality of human and ChatGPT feedback of students’ writing. Learning and Instruction, 91, Article 101894. https://doi.org/10.1016/j.learninstruc.2024.101894
74.
SuW.HuangA. (2024). Rather to receive praise than criticism? Understanding students’ perceptions of feedback valences in academic writing. Innovations in Education and Teaching International, 61(3), 416–425. https://doi.org/10.1080/14703297.2022.2155211
75.
ThorndikeE. L. (1927). The law of effect. The American Journal of Psychology, 39(1/4), 212–222. https://doi.org/10.2307/1415413
76.
ValceaS.HamdaniM. R.WangS. (2024). Exploring the impact of ChatGPT on business school education: Prospects, boundaries, and paradoxes. Journal of Management Education, 48(5), 915–947. https://doi.org/10.1177/10525629241261313
77.
van PinxterenM. M. E.WetzelsR. W. H.RügerJ.PluymaekersM.WetzelsM. (2019). Trust in humanoid robots: Implications for services marketing. Journal of Services Marketing, 33(4), 507–518. https://doi.org/10.1108/JSM-01-2018-0045
WongP.NgP. M.LeeD.LamR. (2020). Examining the impact of perceived source credibility on attitudes and intentions towards taking advice from others on university choice. International Journal of Educational Management, 34(4), 709–724.
80.
Zawacki-RichterO.MarínV. I.BondM.GouverneurF. (2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators?International Journal of Educational Technology in Higher Education, 16(1), Article 39. https://doi.org/10.1186/s41239-019-0171-0
81.
ZhaoX.LynchJ. G.Jr.ChenQ. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197–206.
