| Course | MT362 Artificial Intelligence Applications for the Marketing Professional |
|---|---|
| Unit | Unit 6 |
| Paper type | Seminar reflection |
| Length | About 850 words, 6 pages double-spaced |
| Format | APA 7 student paper |
| School | Purdue University Global |
| Program | BS in Business Administration |
| Updated | October 2026 |
Free sample paper for MT362 Unit 6
A Label Does Not Answer for the Bot: An MT362 Unit 6 Seminar Reflection on Disclosing Automation
[Student Name]
Purdue University Global
MT362: Artificial Intelligence Applications for the Marketing Professional
Unit 6 Seminar Reflection
[Instructor Name]
[Date]
The seminar, classmates and the pet chain are composite; the tribunal decision, statute and research cited are real.
Label Everything, at First
My opening argument was simple. Any message shaped by software should say so: every chat reply from an assistant, every email drafted with an AI tool, every offer chosen by a model. I still think the argument has real strength. A label costs almost nothing, and customers increasingly suspect automation anyway, so saying it openly looks candid. I expected the seminar to debate only where to put the labels.
The Airline and Its Chatbot
The instructor asked us to read the February 2024 decision of British Columbia's Civil Resolution Tribunal in a dispute between a passenger and Air Canada. The passenger, booking travel after a death in his family, asked the airline's website chatbot about bereavement fares and was told he could apply for the reduced fare after travel. The airline's own policy said otherwise, and it refused the refund. The tribunal held the airline responsible for what its chatbot said, rejecting the argument that the chatbot was somehow a separate source responsible for its own statements (Moffatt v. Air Canada, 2024).
I first read the case as support for labeling. If customers knew they were talking to a bot, I argued, they would check its answers.
The customer knew he was talking to a bot and relied on it anyway, so the label had not moved responsibility anywhere.
The Point That Changed My Mind
Andre, who manages a call center for a regional utility, pointed out that the chatbot in the case was plainly automated. The passenger knew he was chatting with a bot, relied on its advice anyway and was right to do so, because the airline had put the bot on its website to answer exactly that kind of question. Disclosure had told him what he was talking to. It had not told him who stood behind what it said, and the tribunal's answer was the airline.
That distinction broke my position into two parts. A label can tell customers whether they are interacting with a system, which changes how they weigh a reply. It cannot transfer responsibility for what the system says. Luo et al. (2019) found that disclosing a chatbot before a sales conversation reduced purchases, even though the chatbot performed as well as experienced staff, which suggests a label also has a cost the business must weigh honestly.
Interaction Versus Drafting
From there I separated two things labels might cover. Talking to a system is the first: a customer chatting with an assistant should know it, because the knowledge affects how the customer reads every answer. California's bot disclosure law, in force since July 2019, already bars using a bot to mislead people in the state about its artificial identity in order to encourage a sale (California Legislature, 2018). Reading text a system helped write is the second. Once a person has read and approved an email, the AI tool that drafted it changes very little about what the customer is receiving, and a label on every such email would say less than it seems to.
What Fernbrook Would Disclose
Applied to the pet chain I work for, the conclusion has three parts. Every chat session opens with the assistant saying, in its first line, that it is software. Emails drafted with AI help and approved by a person carry no label. And a named manager, the director of lifecycle marketing, is answerable for any promise the assistant makes about refunds, deliveries or prices, so that a customer who relies on it, as the airline passenger did, is not left arguing with software.
How the Room Divided
Not everyone moved with me. Two classmates held to blanket labeling, arguing that customers deserve to know about drafting as well as interaction, because a human approver may skim an AI draft rather than read it. That is a fair worry, and it changed one detail of my conclusion: an approved email carries no label, but the approval has to be real, logged by name and date, so that the person who approved it can be asked about it later. A third classmate took the opposite line, calling labels mostly theater, since customers ignore them. I disagree for chat, where the label changes how a customer reads each answer, but I could not show evidence that labels on emails change anything, which is part of why I dropped them.
Doubts Left Open
I am still unsure about one case: offers chosen by the churn model. A customer might reasonably want to know that a discount reflected a prediction that they were about to leave. My new position does not answer that, and I would rather let the pilot's complaint data tell us whether customers care than pretend the seminar settled it.
References
California Legislature. (2018). Bolstering Online Transparency Act (S.B. 1001). California Legislature.
Luo, X., Tong, S., Fang, Z., & Qu, Z. (2019). Frontiers: Machines vs. humans: The impact of artificial intelligence chatbot disclosure on customer purchases. Marketing Science, 38(6), 937-947. https://doi.org/10.1287/mksc.2019.1192
Moffatt v. Air Canada, 2024 BCCRT 149 (2024).
The Purdue Global MT362 Unit 6 instructions
The Unit 6 seminar debated how organizations should tell customers about their use of AI, and the reflection reports, in the student's own voice, how that debate went for them. The reflection should state the position the student brought, the argument or case that tested it and where the student now stands. Instructors expect legal cases to be described accurately and briefly, with the court or tribunal and date. The opposing argument should be credited to the classmate who made it. A strong reflection applies its conclusion to a real or course business in specific terms. Remaining doubts are welcome and often show more judgment than a tidy ending. A change of view is not required, but the reasoning must be visible either way.
How the MT 362 Unit 6 example is put together
The writer's opening view is stated with its real appeal. The tribunal case is summarized in a paragraph, followed by the writer's first reading of it. Andre's point is isolated under a highlighted line about the customer who knew he was talking to a bot. The writer then separates interaction from drafting, citing Luo and colleagues on the cost of disclosure and California's bot law. A section applies the conclusion to the pet chain in three parts. Another records how classmates divided, including the logged-approval detail that changed the conclusion. The last paragraph leaves open a question about disclosing model-chosen offers, left for the pilot's complaint data.
Reading the MT362 Unit 6 grading rubric
Seminar reflections here are mostly graded on the honesty of the starting position, the accuracy of the case discussion, the clarity of the change and the specificity of the application. A first view stated fairly, with its strengths, earns credit for candor. The case should be summarized correctly and cited. Rubrics reward a turning point tied to a specific argument. Separating two things a policy might cover, here interaction and drafting, shows analytical growth. Application to a named business, with concrete rules, demonstrates transfer. Open doubts presented as questions for evidence show judgment. The tribunal decision, the bot law and the Luo study each need a correct APA 7 entry.
MT362 Unit 6 help: mistakes that cost points
Reflections on disclosure debates often retell the Air Canada case in detail and spend little time on the writer's own thinking. Others present a final position as if it had always been held. A common gap is applying the conclusion only in general terms, such as being transparent, instead of saying what the business will label and who answers for the system. Some reflections overstate what the tribunal decided. Before drafting, write your first position and your final one as two single sentences. Then identify the one argument that moved you from the first to the second. That argument, examined closely, is the center of the reflection.
Get an MT362 Unit 6 example written to your instructions
Disclosure debates run differently in each section. Describe what yours argued in Unit 6, whether you joined live or wrote the alternative, the stance you brought and whatever shifted it, even partly. Add the reflection rubric; the custom reflection keeps your voice, follows in 24-48h, free as a first sample, and dates any case it cites. The paper above is an original model document written by our desk, not a submitted student paper and not an official Purdue University Global document.
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MT362 Unit 6 questions, answered
What if my view did not change during the seminar?
That can still work. Reflect on the strongest argument against your position and explain why it did not persuade you, or describe how your reasons became sharper even though the conclusion held. Graders usually look for engagement with opposing views, and a position that survives a serious challenge, explained honestly, shows that engagement.
Can I cite legal cases in a marketing reflection?
Yes, when they bear on the debate, and disclosure debates often turn on them. Name the court or tribunal, the year and the holding accurately, and say whether the ruling binds the business you are discussing. A foreign decision can illustrate a principle without governing a US company, and the reflection should say which role it plays.
Where can I find a free MT362 Unit 6 sample paper?
The full MT362 Unit 6 seminar reflection appears here, narrowing a blanket labeling position after debating the Air Canada chatbot ruling.
What did the Air Canada chatbot ruling decide, as described in the MT362 Unit 6 sample?
The British Columbia tribunal held the airline responsible for its chatbot's wrong advice on bereavement fares, rejecting the idea that the chatbot answered for itself.
Does labeling a chatbot shift responsibility for what it says?
Not according to the reasoning in this sample: the passenger knew he was using a bot and relied on it anyway, and the airline remained responsible, so a label informs customers but does not move accountability.