The Uncanny Valley and the Future of AI in Mentoring Program

By Jean Rhodes

The advent of artificial intelligence (AI) has sparked a revolution in our lives and society, and mentoring is no exception. While AI holds immense potential to improve mentoring processes, it is crucial to approach its integration thoughtfully, leveraging its strengths while mitigating potential risks.  There are ethical concerns around the collection and use of personal data for AI training and clear policies and protections on data handling, privacy, security, and opt-out options are essential. AI models can also inadvertently perpetuate societal biases present in their training data, leading to discriminatory mentor-mentee matching algorithms and personalized recommendations. Addressing bias requires diverse and inclusive data collection. There are significant risks associated with AI in mentoring is the temptation to replace human mentors with chatbots. Research has shown that the “uncanny valley” effect, a phenomenon where humanlike robots or AI systems elicit feelings of unease or revulsion, can have a detrimental impact on human-AI interactions (Ciechanowski et al., 2019). Attempting to substitute human mentors with chatbots, no matter how realistic and interactive, will only deepen young people’s alienation and craving for genuine human connection (Mathur & Reichling, 2016),  as they are unlikely to provide the empathy, emotional intelligence, cultural context, and nuanced guidance that human-to-human mentoring relationships can offer.

 Leveraging AI for Augmentation, Not Replacement

Rather than replacing human mentors, AI should be strategically deployed to augment and enhance the mentoring experience. The following are key areas where AI can be leveraged effectively:

Data Analysis and Natural Language Processing: AI excels at analyzing vast amounts of data from mentoring interactions, surveys etc in ways that can produce valuable insights on program effectiveness, successful mentoring strategies, skill development needs, and areas for improvement.  A promising area that MentorPRO is working on is using natural language processing (NLP) for detecting signs of self-harm, mental health issues, or other concerning patterns (Arowosegbe & Overlade, 2023). Likewise, AI-powered sentiment analysis can even provide mentors with real-time feedback on their communication approach, enabling them to adjust their tone, language, and overall approach to better resonate with the mentee By integrating data from various sources, such as texts, weekly check-ins, goals, surveys, and wearable devices, AI can provide a comprehensive, real-time view of the mentee’s well-being and progress. By seamlessly integrate data from various sources AI can provide mentors with actionable insights and recommendations tailored to the each mentee.

Personalized Matching: One of the biggest promises of AI in mentoring is the ability to leverage data and algorithms to intelligently match mentors and mentees based on their skills, interests, goals, personalities, and other factors. This can lead to more compatible and productive mentoring relationships.

Resource Curation: AI can also provide personalized recommendations for learning resources, development opportunities, or discussion topics tailored to each mentee’s unique needs and growth areas identified through data analysis. By anticipating potential issues or knowledge gaps, AI can provide mentors with “just-in-time” training and guidance, ensuring they are equipped to handle specific situations as they arise, rather than relying solely on upfront “just-in-case” training. Within this context, AI can provide trainings that bridge language and cultural barriers by providing culturally-grounded suggestions and resources.

Eventually, AI can be used to power learning platforms like MentorPRO Academy to deliver personalized learning experiences by adapting content, difficulty levels, and learning pathways based on the individual mentee’s progress, strengths, and weaknesses identified through data. Interactive AI tutors like Khanmigo can provide practice, feedback, and coaching on specific skills.


AI tools can provide mentees with on-demand guidance and support, answering common questions or directing them to relevant resources when human mentors are unavailable. AI can also help human mentors manage larger numbers of mentoring relationships by automating administrative tasks like scheduling, tracking progress, and providing reminders.

These are just a few ways that AI can be used to enrich (and not replace) mentoring relationships in the years ahead. I’m sure there’s many more, maybe I should ask ChatGBT. By leveraging AI in these and other areas, mentors can benefit from improved insights, personalized support, and streamlined access to data analysis and resources, ultimately improving the quality and effectiveness of the human-to-human mentoring experience!


Ciechanowski, L., Przegalinska, A., Magnuski, M., & Gloor, P. (2019). In the shades of the uncanny valley: An experimental study of human–chatbot interaction. *Future Generation Computer Systems, 92*, 539-548.

Mathur, M. B., & Reichling, D. B. (2016). Navigating a social world with robot partners: A quantitative cartography of the Uncanny Valley. Cognition, 146, 22-32.

Arowosegbe, A., & Oyelade, T. (2023). Application of Natural Language Processing (NLP) in Detecting and Preventing Suicide Ideation: A Systematic Review. *International Journal of Environmental Research and Public Health, 20*(2), 1514.