In every professional and personal sphere—be it business, medicine, engineering, or parenting—we inherently need a mentor. However, we don’t need a mentor who simply validates us; we need one who scaffolds our progress step-by-step. A true mentor is one whose stance doesn’t shift instantly with our every response. Despite being flexible and open to different perspectives, they do not easily abandon their position based solely on our feedback.
Mentorship is, at its core, an educational role, and it must therefore operate on established pedagogical principles. The emergence of any new technology can reshape both concepts and practices.
One of the most profoundly impacted areas over the last two years is “Education.” In the era of Artificial Intelligence and the race to deploy Large Language Models (LLMs), educational systems have felt the greatest impact. As global giants compete for AI investment, educational institutions are equally racing to research the qualitative and quantitative use of AI.
Central to this is the concept of “Mentoring and Mentorship.” As the name suggests, it refers to guiding the flow of thought and performance of a human user.
Since this process involves providing specialized knowledge to achieve a specific result, we can say a mentor is akin to a “teacher” in a formal classroom, and mentoring is fundamentally an educational concept.
Redefining Mentorship in the Age of LLMs
Both the term and the practice of mentorship have been transformed by LLMs like GPT and Gemini. Yet, despite the ease they offer, this shift is open to critique and raises significant concerns.
Choosing an AI mentor is far more difficult than choosing a human one, because an AI is an ultra-fast intelligent machine lacking experiential history, focused instead on ultra-heavy data processing.
Among the hundreds of apps recommended daily, three giants claim this path:
• Gemini 3 Pro: The “Analytical and Realistic” mentor. Accesses live data and all your personal files.
• ChatGPT 5.2: The “Strategic and Methodological” mentor. Provides a framework for your mental chaos.
• Claude 4.5: The “Literary and Considerate” mentor. Focused on human-like tone and output quality.
According to February 2026 statistics (LMSYS Arena & Artificial Analysis), ChatGPT 5.2 leads in reasoning intelligence, while Gemini 3 Pro excels in memory and processing speed.
However, in mentorship, quantitative superiority is not the whole story. While Gemini is touted as analytical and exploratory, I believe further investigation is needed:
1- Which model analyzes, and on what topics?
2-Quantitative and mathematical? Qualitative and characteristic? In what context?
3- Similarly, if ChatGPT is “strategic,” can logic truly be separated from data critique? Is “strategizing” not dependent on one’s unique mental background? And what, exactly, does a “considerate writer” mean in this context?
Scaffolding: Human Mentoring vs. Large Language Models
Let us compare the two. The most striking feature of a human mentor is their experiential background and their specific perception of that experience—which includes an interpretation and an emotional component.
A human mentor provides an empirical direction shaped by cognitive and emotional dimensions alongside their knowledge.
Conversely, an LLM is a data repository pulling from websites in real-time. It lacks lived experience and cannot integrate intuition or “gut feeling” into a decision-making system.
While AI excels at helping with “brainstorming” by providing a vast range of references instantly, it suffers from a fundamental flaw: the absence of personal perception and the emotional weight that is vital in mentoring.
Furthermore, the stages of guidance differ. Human mentoring is a gradual, step-by-step flow. A human mentor assesses your capacity and scaffolds you accordingly. In contrast, with GPT or Gemini, there is no “scaffold.” Education is not incremental, and there is no cognitive challenge.
The model provides a massive amount of information in one or two steps. The user is pleased with the instant result, but a “missing link” remains: the user becomes perpetually dependent on the AI. They cannot independently solve subsequent challenges because they never underwent the necessary experiential and cognitive stages.
A Biological Analysis
Biologically, learning and acquisition are based on protein exchange at the neural level. This occurs when an organism encounters challenging and unknown subjects.
According to the laws of evolution, the brain automatically triggers biochemical reactions to resolve these challenges, ultimately leading to “Learning” and “Adaptation.”
When a human mentor gradually confronts a user with their errors and potential consequences, they provide the necessary neurobiological challenge.
This scaffolding is exactly what an evolved brain requires for “Deep Learning” to occur. However, when dealing with a “Digital Mentor,” this cognitive elasticity disappears. The process of “Cognitive Trial and Error” is compressed into a high-speed instant.
The digital mentor dictates, and the user merely mimics and obeys. This pattern does not align with our biological necessity. Therefore, this process cannot be considered natural mentoring; it is merely “Modeling.”
Conclusion and Critical Perspective
In recent years, the surge of trend-driven discourse surrounding education and Artificial Intelligence has led to the analysis and judgment of fundamental pedagogical concepts without sufficient theoretical or empirical backing.
The oversimplification of concepts such as Mentoring, Scaffolding, and Large Language Models (LLMs) risks reducing them to mere buzzwords—widely used yet hollow. Therefore, it is essential that this movement be examined by specialists grounded in scientific evidence and core educational principles, ensuring that superficial, word-centric views are replaced by rigorous, research-based analysis.
In this article, mentoring was addressed as a dependent subset of Education—a concept that, whether in formal settings like schools and universities or in informal domains such as personal life, healthcare, industry, and business, remains rooted in the profound foundations of the learning process. Furthermore, the relationship between scaffolding, mentoring, and LLMs was scrutinized.
Based on the arguments presented, the primary challenge is not the necessity of digital mentors, but rather that these mentors are currently simulated versions, not complete replacements for human mentors. In this regard, the following questions demand serious investigation and review:
• Can development companies scientifically bridge the gaps identified in this article?
• Is it possible to integrate a form of experiential history, historical memory, and emotional/perceptual dimensions into digital mentors to truly impact a user’s deep learning process?
• Can they activate the biochemical mechanisms and cognitive friction necessary for deep learning and adaptation to new situations within the user-system interaction?
• How deep and operational is these companies’ understanding of Scaffolding, and can they genuinely integrate it into innovative design?
If a precise understanding of these gaps and challenges is formed, the digital mentors developed by tech giants could evolve beyond passive information packages. By leaning on the Sciences of Learning, they could redesign the process of educational guidance into one that is both challenging and incremental.
The core issue is not the necessity or lack thereof of the digital mentor; the issue is whether it can recreate the challenge, the experience, and the gradual process of learning, or if it will simply replace growth with speed.
References
1. Primary AI Benchmarks (2026):
•LMSYS Chatbot Arena (The industry-standard for human-preference and helpfulness ranking).
2.MMLU-Pro (The leading benchmark for advanced reasoning and multi-step logic).
3.Gemini Technical Reports 2026 (Official performance metrics for real-time data latency and multimodal accuracy).
2. Specialized Publications by the Author:
• Ferdosipour, A. (2026). Choosing an AI Mentor That Challenges Your Mind: My Statistics.
• Medika Life (2025/2026). What 2025 Taught Us and What 2026 Will Demand.
• Medika Life (2026). Why Biological Learning Demands the Friction We Seek to Delete.
