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As the leader of product here at Mentor Collective, I’ve seen firsthand how industry insights reinforce our core belief: outcomes improve dramatically when learners and users are involved in shaping solutions. Our recent launch of Conversation Sparks was shaped by user surveys, early prototypes, and a decade of research on what mentees and mentors expect from technology. Hearing the industry mandate for this collaborative approach has only deepened my motivation and excitement to continue engaging our user base—listening to what truly stops them from achieving their goals and how technology can help overcome those obstacles.
This aligns with a key lesson I took away from the Agentic AI and the learner Experience conference (hosted by ASU): AI’s most essential lessons aren’t just about crafting great prompts or understanding algorithmic placement—they’re about responsible, human-centered design.
Amidst all the technical talks, the conversation shifted to a vital question: how do we democratize AI design to ensure tools are responsible, scalable, and grounded in human values? The learners—the true co-creators of tomorrow’s AI—are setting a profound new standard for how technology must serve them.
Lesson 1: The Expectation of Total Context and Memory
For learners, the value of an AI agent is its ability to eliminate the small, frustrating inefficiencies of institutional life. Their expectation is simple: the AI should know everything about me and use that knowledge to make my life easier.
This translates into core demands for builders of agentic AI:
- Friction Reduction and Integration: Learners cannot understand why they must repeatedly enter the same data into different systems. They expect AI to pull their data from other systems, so they don’t need to re-enter. They expect AI to reduce friction and make content easily accessible, delivering support 24/7 with no waiting.
- Engineering the Persona: When learners serve as prompt engineers for projects, they heavily invest in training the AI to use a voice that is warm, familiar, and friendly. This shows that the emotional design (joyful, inclusive tone set by prompt engineering) is a critical factor for adoption.
Lesson 2: The Mandate for a 50/50 Partnership
The learners' vision for AI is not one of automation, but of augmentation. They do not expect AI to act on their behalf; instead, they see it as a partner responsible for making it easy for them to achieve forward progress toward their goals.
- Co-Creation and Responsibility: This shift from fear to confidence in AI reflects the profound trust learners place in our ability to deliver. They see themselves as responsible for AI outputs and expect AI to be centered on humans, focused on enhancing their lives. Learners want a voice and tone that is encouraging, casual, and familiar—building trust through humane, responsible design.
Lesson 3: Focus on "Human Centered Design"
What was clear from the colleges, universities, and technologists at the conference is that their outcomes were dramatically better when they involved learners and users in their processes. This collaborative methodology ensures that tools remain focused on human-centered design.
The Path Forward: Building AI as Partnership, Not Replacement
The mandate from learners is clear: AI must remember context, reduce friction through seamless integration, speak with warmth, and operate as a true 50/50 partner—augmenting capability rather than replacing it.
These insights confirm that the best AI solutions emerge from co-creation with the people they serve. At Mentor Collective, this philosophy guides everything we build, from Conversation Sparks to our broader AI strategy.
Want to learn more about how we're putting these principles into practice? Explore our approach to AI in mentorship and see how we're building on the foundation we laid in our previous exploration of AI's role in human connection.
Mentorship for All,
Annemieke Rice, Mentor Collective VP of Partner Success