Artificial intelligence (AI) has quickly moved from novelty to inevitability in higher education. Students are already using AI tools, sometimes productively, sometimes problematically, often without clear guidance from faculty.
The question is no longer whether AI belongs in our courses, but how intentionally we choose to use it.
In my introductory marketing course, I recently designed a project that integrated AI, not as a shortcut, but as a collaborative thinking partner. The goal was not to outsource thinking to a machine, but to scaffold student creativity, strategic reasoning and sustainability-minded decision-making at a level they would not yet be able to reach independently. What follows are key lessons from that experience that may help other faculty design AI-enhanced assignments that truly support learning.
Identifying appropriate uses of AI in course design
Before introducing AI into any assignment, faculty must first ask a foundational question: “What do I want students to learn?” Technology, including AI, should always serve the learning outcomes, not the other way around, reinforcing the importance of using a formal framework such as TPACK (Technological, Pedagogical and Content Knowledge). Rather than asking, “How can I use AI?” the more productive question is, “Where can AI meaningfully enhance students’ engagement with content and skills?”
This article is part of a biweekly series provided by the Instructional Technology Council, an affiliated council of the American Association of Community Colleges.
Related to the marketing campaign task in my course, the primary learning outcomes centered on creativity, strategic thinking and sustainability-oriented marketing decisions. Students were enrolled in their first marketing class and were not yet prepared to independently produce a comprehensive marketing plan. However, with AI acting as a collaborator (generating ideas, offering alternative perspectives and prompting refinement) students could engage in higher-level thinking without being overwhelmed by technical gaps.
In this context, AI was not replacing learning; it was extending students’ cognitive reach.
Positioning AI as a collaborative partner
One of the most important design decisions I made was explicitly framing AI as a collaborator, not an answer engine. Students were required to interact with AI iteratively, refining ideas and making decisions based on their own judgment. The intent was that they would have a dialogue with the AI thought partner to refine ideas based on their own experiences, as they would as part of a team in the workplace.
To support this, I provided students with a short instructional video on prompt engineering. Rather than assuming students would intuitively know how to communicate effectively with AI, the video walked them through how to:
- Define a clear role for the AI.
- Provide sufficient context.
- Ask for specific types of output.
- Iterate and refine prompts based on results.
Without guidance, AI interactions tend to be shallow or misaligned with instructional goals. Teaching prompt design is, in itself, a valuable transferable skill, one that mirrors strategic communication and problem framing in professional contexts.
The importance of a rich, purposeful scenario
Another essential component of the project was a detailed scenario outlining the company profile and the purpose of the marketing task. Students were given background information about a fictional organization: its mission, values, target market and sustainability goals.
This scenario served multiple functions. It anchored the AI’s responses, provided realism and ensured that student outputs were not generic, while leaving room for student agency to develop the business and marketing, according to their own values. More importantly, it forced students to make strategic choices within constraints, one of the core competencies of marketing practice.
Using the scenario and their interactions with AI, students produced the following deliverables:
- An executive summary
- A target audience analysis
- Marketing objectives
- Marketing strategies and channels
- Key messaging
- A reflection on their process and use of AI
The reflection component was especially important, as it required students to articulate how AI influenced their thinking, where it helped and where human judgment was still essential. Those reflections are discussed in more detail later.
Rethinking assessment in an AI-enabled world
The rise of AI has exposed the limitations of traditional assessments. If a student can generate a passable essay in minutes, then the assignment may no longer be measuring what we think it is measuring.
This reality invites faculty to think creatively about how students demonstrate learning outcomes. Projects, scenarios, reflections and iterative design tasks can assess skills that are difficult to outsource entirely to AI, such as judgment, synthesis, creativity and strategic alignment. Reimagining our pedagogy and abandoning the traditional lecture-test structure, that never really did work, has never been more important.
Equally important is the role of the rubric. When AI is part of the learning process, rubrics must explicitly detail the skills instructors care about. For example, in requiring students to dialogue with the AI collaborator, I made sure my rubric explicitly and transparently detailed the requirement that they could not simply ask questions and use the results provided; that they had to offer their own ideas and refine what was produced for them. The inclusion of criteria in the rubric where students provide their transcripts allowed me to evaluate the depth of their interactions with their collaborator.
In my project, I discovered a misalignment between my intentions and my assessment design. Although I created a video to teach students how to write effective prompts, only one student actually followed that structured process. Some students copied and pasted the scenario verbatim into the AI tool. Others used brief, perfunctory prompts that yielded shallow results.
Because my rubric did not explicitly assess initial prompt quality, students had little incentive to engage deeply in that aspect of the task. This information was used to iterate the rubric and task instructions for the next time I teach the class. This type of continuous improvement process of reflection and iteration is a best practice in teaching.
The value of the reflection
While the final marketing deliverables demonstrated students’ creative and strategic thinking, the reflection component provided the clearest window into their learning. In an AI-enhanced assignment, the product alone rarely tells the full story. Reflection allowed me to see how students used AI, not just what they produced.
By requiring students to reflect on their interactions with AI — what they asked, how they refined prompts, which suggestions they accepted or rejected, and why — they were forced to slow down and make their thinking visible. This metacognitive step helped distinguish genuine learning from superficial use of the technology and reinforced that AI is a tool for supporting, not replacing, human judgment.
Student reflections also revealed growth in ways that traditional assessments often miss. Several students described how AI helped them to think outside the box of their own ideas, sparking their own creativity and making the brainstorming process feel more dynamic. Others noted how the iterative process helped them better understand how to tie the task into their own ideas to reach target audiences and apply their knowledge.
Including reflection as a graded component reframed AI use as an intentional, accountable process rather than a shortcut. It also created a space for students to articulate ethical considerations, recognize the limits of AI-generated content and acknowledge their own role as decision-makers. In an educational landscape where AI can generate polished outputs with ease, reflection remains one of the most powerful tools for ensuring that learning, not just production, remains at the center of our assessments.
Moving forward with intention
AI presents both a challenge and an opportunity for higher education. When used uncritically, it can undermine learning. When used thoughtfully, it can elevate it, allowing students to engage in complex, creative and strategic work earlier in their academic journeys.
For faculty, the path forward lies in intentional alignment: aligning AI use with learning outcomes, aligning assignments with authentic skills and aligning rubrics with what truly matters. When those pieces come together, AI becomes not a threat to academic integrity, but a catalyst for deeper learning.
