Working on the AI in Mentoring project has been more than just a professional challenge – it has been an opportunity to merge my background in learning & development, coaching, and mentoring with the evolving world of artificial intelligence. At New Mindset Coaching & Training (NMCT), we have been developing instructional materials to support work-based learning (WBL) mentors, equipping them with the knowledge and tools to integrate AI into their practice.
For many, AI remains an abstract concept, often seen as either a futuristic promise or a threat to human connection. But in this project, we are exploring how AI can enhance – not replace – the mentor’s role. The focus is on practical applications, ensuring that AI serves as a tool to improve the mentoring experience rather than as a substitute for human insight.
Developing these materials has required me to bring together multiple perspectives. As a communicator, I work to ensure the content is clear, engaging, and accessible for mentors across different industries. As a coach and trainer, I focus on practicality, ensuring that what we develop can be applied in real mentoring relationships. And now, like many others, I am stepping into the role of an AI explorer, learning how these technologies can meaningfully support education and mentorship.
One of my key tasks has been examining how AI can enhance WBL mentorship. This means looking at specific use cases, such as:
- Using AI-generated prompts to help mentors ask more insightful questions.
- Leveraging AI-powered sentiment analysis to detect early signs of disengagement or concern.
- Automating administrative tasks so mentors can focus on what truly matters – building trust and guiding their mentees.
The foundation of our materials is rooted in both theory and real-world experience. We begin by exploring the basics of work-based learning mentorship in the age of AI, ensuring that mentors understand the context in which they are operating.
We then provide a refresher on core WBL principles, emphasizing the role of mentorship in helping young professionals transition into the workforce. This includes insights from Belgium, Bulgaria, and Spain, drawing on data from CEDEFOP and the European Commission. The key message is clear: a skilled mentor can turn even a challenging placement into a meaningful learning experience.
In the next phase, we focus on the essential competencies of a successful WBL mentor, aligning them with the European Mentoring and Coaching Council (EMCC) framework. We highlight:
- Social and interpersonal skills – The ability to build trust and rapport.
- Organizational and managerial skills – Structuring the mentoring process effectively.
- Pedagogical skills – Guiding learning in a way that is adaptive and engaging.
- An individualized approach – Recognizing that each mentee has unique needs and aspirations.
A key takeaway from this section is that mentoring in a WBL setting is both an art and a science, requiring a balance of structure and flexibility.
At NMCT, we want to make one thing clear – AI is not here to replace human mentors. Instead, it is a tool that can free up time, reduce administrative burdens, and enhance the mentoring experience. AI cannot replicate the depth of human relationships, but it can help mentors focus on what they do best – guiding, supporting, and inspiring.
Those who embrace AI as a complementary tool will gain a significant advantage, not just in efficiency but in their ability to personalize and deepen their mentoring approach. The future of mentoring is not about choosing between AI and human connection – it is about integrating the best of both.
