Collaborating with Academics Who Are Unsure About AI
Building trust and confidence in AI-assisted course design
Introduction: A Shift That Needs Stewardship
In our work at Learning Design Solutions, one of the most common — and entirely understandable — reactions to generative AI from academics is caution.
Many subject matter experts (SMEs), especially those deeply experienced in their discipline and teaching, are sceptical about whether AI can handle nuance, academic standards, or pedagogical intent. Others simply feel overwhelmed by yet another emerging technology in higher education.
This post explores how we meet that scepticism with structure, transparency, and sound pedagogy — and how thoughtful collaboration can help academics move from uncertainty to confidence in AI-supported course design.
Understanding the Concerns
We hear the same questions time and again:
“It’s not accurate.”
“It won’t sound like me.”
“It’s just going to replace the teacher.”
“How do we know it’s not plagiarising?”
“I don’t want to lose control of the content.”
These are all valid — and in some cases, justified — concerns. Used naively, AI can produce superficial or unreliable results.
Our job isn’t to dismiss these anxieties. It’s to address them with clarity, structure, and safeguards that preserve the academic’s ownership while allowing us to work more efficiently and creatively.
The Key Principle: AI Assists, Humans Own
From the very start of every project, we make one thing absolutely clear:
AI assists — it doesn’t replace.
That means:
• The SME remains the module author.
• All AI-generated drafts are reviewed and edited by humans.
• The final say always rests with the academic.
• Prompts are guided by intended learning outcomes, level, and context.
This reassurance creates a safe environment for experimentation — where academics can explore the potential of AI without losing control of their voice or intellectual integrity.
How We Build Confidence in Practice
At Learning Design Solutions, we build academic confidence in AI through transparency, collaboration, and clear role definition.
Early Orientation
At the start of every collaboration, we are open about how AI supports our design workflow. We explain that it is a collaborative design tool — one that speeds up the drafting process and keeps alignment tight, but still relies on expert human oversight. We discuss the boundaries of its use and how it fits within our principles of constructive alignment, backward design, and UDL.
Planning First
Once the SME has articulated their vision and goals for the module, we use AI to help produce a substantial, structured plan for the entire course. This plan draws on the SME’s outline, programme learning outcomes, and assessment strategy. We then bring this first draft back to the SME for their review and correction.
This stage is vital. It shows the academic that the AI’s role is to support and scaffold their expertise, not to replace it. The process also introduces them to how AI can help express ideas quickly and coherently, while leaving them firmly in control of content and tone.
Focus on Expertise
The learning designer handles all prompting, keeping the academic focused on content and quality, not on process or technology. While we’re aware that many academics now use AI independently for tasks such as drafting notes or rewording explanations, we don’t ask or expect them to do so. Our focus is on drawing out their subject expertise and helping them express it effectively for online learning.
Managing Tensions and Maintaining Ownership
One of the unexpected challenges we’ve encountered is that, as our capability has increased, so too has the risk of academics stepping back too far. Because our AI-enabled design process can generate drafts for almost every part of a module, some SMEs have assumed that their contribution is minimal — or have reviewed content only superficially.
We’ve learned to mitigate this in several key ways:
1. Clear roles and responsibilities. We make explicit that the SME is responsible for writing the teaching content — the materials that convey new disciplinary knowledge directly to students. This includes micro-lectures and “expert insights” (the extended on-screen content we transform into interactives). The learning designer’s role, supported by AI, is to structure, storyboard, and format this content so that it becomes engaging, accessible, and aligned.
2. Ownership and professional visibility. The SME’s name appears on the module as its author. They feature in the course videos, micro-lectures, and introductions throughout the module. This sense of visibility — and professional accountability — encourages them to take genuine ownership of the material. They must be confident that what goes out under their name is accurate, rigorous, and truly representative of their academic ideas.
3. Institutional leadership and support. Success also depends on leadership from the client institution reinforcing these expectations. When programme leads and managers make it clear that SMEs remain accountable for the academic quality of content, it helps maintain engagement and standards.
4. Rigorous quality assurance. Every module undergoes our standard QA process, including an academic review by a senior colleague or another academic involved in the wider project. This ensures that modules not only meet institutional and pedagogical standards but also reflect the intellectual integrity of their authors.
Together, these measures ensure that collaboration remains balanced — that the AI is used to enhance academic authorship, not to replace or dilute it.
Why This Approach Works
• Transparency builds trust. Academics understand the process and their role within it.
• AI accelerates structure. A full course plan can be produced and reviewed early, helping everyone visualise the path ahead.
• Expertise stays central. The SME remains the author of the academic narrative.
• Quality improves. Iteration between AI drafts, SME feedback, and LD oversight raises both coherence and alignment.
• Accountability ensures rigour. Visible authorship and formal QA processes preserve standards and ownership.
Final Thoughts
Integrating AI into course design isn’t just a technological change — it’s a relational one. It depends on trust, clarity, and a shared understanding of roles.
When academics know that AI is there to support, not supplant, their expertise, they begin to see its value as a design partner. And when learning designers approach this work with transparency, sound pedagogy, and institutional backing, AI becomes not a threat but a catalyst for better, more efficient collaboration.
At Learning Design Solutions, this is the balance we strive for — combining innovation with integrity, and pedagogy with partnership.
Explore More or Talk With Us
Want to explore how AI could support your academic teams — without overwhelming them?
Read the AI Pedagogy for Online Courses series: https://www.learningdesignsolutions.co.uk/blog