Executive Summary
Rethinking Upskilling-Leveraging Agentic AI for Scalable, Bespoke Learning
For over five decades, EdTech has promised to revolutionize learning, yet enterprise workforce training still struggles to meet the individual needs of learners in a scalable and impactful way. Most solutions tackle surface issues like engagement and gamification but miss in addressing the deeper challenges of delivering adaptive, meaningful, and personalized learning at scale. The result is a one-size-fits-all approach that leaves learners unfulfilled, experts experience boredom, novices experience anxiety with learning, workplace managers want to reduce time away from work to learn and L&D teams overstretched in today’s rapidly evolving workplace.
Generative AI presents a pivotal opportunity to change this paradigm—but deploying AI and focusing it on a corpus of knowledge alone isn’t enough. The true potential lies in creating adaptive learning systems that respond dynamically to individual learner progress, goals, and preferences. Mentor126 rises to this challenge by harnessing agentic AI to enable scalable, bespoke learning experiences that are based on proven learning science principles and support lifelong growth instead of temporary engagement.
At Mentor126, we believe in moving beyond flashy and superficial personalization experiences to transformative education that evolves alongside the learner. Our “Just in time. Just enough. Just for Me.” philosophy ensures learners receive precisely what they need, when they need it, fostering autonomy and long-term success that uses a distributed framework that enables anytime, anywhere availability of upskilling like electricity offers ubiquity.
For enterprises seeking meaningful change in their workforce training, this approach delivers both scalable efficiency and 1:1 upskilling impact—turning the promise of EdTech into a reality.
INTRODUCTION
Education has been the target of technology companies for 50 years. It is still one of the most popular spaces for technological innovation and application. Like hospitals that will always have patients, in education, education will persist because there will always be a need to teach someone, something. There is a revolving door of companies looking to get incrementally making education more efficient, more effective, and more engaging. While showing improvements, are they solving the right problems in the right way? Or just offering an incremental benefit so a subset of the workforce. Are they just another tool to treat a symptom, and not treat the cause? The education value chain is ripe for disruption and with Agentic AI we are at a pivotal time to use new tools, in the right way, to actually make an impact.
Technology has long promised to improve the delivery of educational content, make the delivery of content more efficient, and make it more effective by helping learners reach specified outcomes. The most popular way to do this is to address two major challenges facing education programs right now: there’s not enough time and resources to meet the individual needs of increasing numbers of learners, and learners are forced to all follow the same path at the same speed as everyone else. In the workforce, employers want employees with the necessary skills; they believe that upskilling and reskilling is needed but don’t want to turn off the productive revenue generation for humans to learn.
Many researchers and theorists over the last 60 years in education will agree, to some extent, that learning should consider the individual needs of the learner. Yet traditional, historical, formal education systems consistently fail to do this well. It is a common concern among researchers that education was never meant to teach to the scale it does today, but I digress. This issue is why many companies use various forms of Educational Technology (EdTech) to help tailor the education experiences to specific learners. This fix is the ‘low hanging fruit’ of educational learning systems. And it makes sense. If learners can get content presented to them in diverse ways, to match their learning preferences or their interests, they may be motivated to keep learning. EdTech is wonderfully suited for companies to create a new tool or technology that can make learning more interesting, personal, “gamified,” or use more videos or songs, for example. There is a lot of research into the effectiveness of these short-term engaging, exciting tools. But they are limited in their long-term effectiveness. It’s like instant gratification, or external motivation. These strategies don’t change how people learn, nor do they teach them how to learn, and be self-directed or autonomous. If they did, we’d see widespread adoption of one tool or one type of tool and we’d see a difference in schools. Still to this day, not one method or tool has stood out as particularly effective across all levels and across all learners, the 100%. Like most things, there is no magic pill or bullet or one tool or one program that can work for all learners. To be truly effective, the technology available to the EdTech industry could be designed to create unique technology, tools, and processes, to meet specific needs of individual learners. To clarify, what the industry doesn’t need is another tool that can be used by all learners, but instead, a unique tool for individual learners! With the technology available today, with the introduction of AI into the educational space, the opportunity may finally be here.
As AI has gained public attention and the public has gained access to AI tools, many people and companies are looking to use AI in new ways to solve educational problems. But as many companies adopt and develop AI, they are continuing to develop a single tool or method to be used by all learners. For at least the past 20 years, the long-term effectiveness of long-term learning has yet to appear. Companies and education programs continue to sift through solutions, trying one thing after another, constantly looking for that “magic pill.” Many of the enterprise decision makers will, instead of admitting the latest tech solution didn’t work as planned, look for a solution as part of a comprehensive program based on educational foundations, they search and select another product with a sales pitch to solve another problem that may or may not exist. The idea is that the new tool must be better simply because it’s different from the prior tool that had failed. Workplace Ed Tech has been around for over 20 years, heard numerous promises and claims, and yet here we are: still looking for more effective solution.
In addition to being too general, there is another major limitation with current EdTech tools and programs: they are static. The vast array of current and historical EdTech tools are like photographs, snapshots in time. They treat the one solution in the one specific situation, for a narrow set of learners, in the moment. This contradicts the fact that the needs of the learner are constantly changing; preferences change, styles change, people change, and situations change. The current educational systems would require teachers to constantly adapt and address different variables in the classroom. Whether it be from lack of resources, support, or background and training, teachers and instructors can’t keep up with the demands of increasing students and diverse needs.
Instructors are responsible for more learners while budgets and their resources decrease. EdTech providers take advantage of this and focus on developing tools, now with AI, that assist the training or the automating of tasks or processes in these situations. Other AI solutions help the learner progressing through the course schedule aiding in more consumption of content or compliance in mandatory coursework. AI brings a new opportunity unlocked by technology addressing in new ways, a solution they’ve been attempting to address since its inception. Solution providers are developing new tools using AI and claim they create some variation of personalized, individualized, differentiated or adaptive learning experiences. But what do all these terms mean? And are the companies doing what they say they are doing in a way that will actually result in meaningful change for learners? Which one is better? Let’s look at the definitions.
DEFINITIONS
Individualized learning is a process where an instructor adjusts the pace of learning to meet the needs of individual students. The academic goals are the same for a group of students, but students can progress through the curriculum at different speeds. In this case, students have more time and are not forced to follow a standardized timeline. This is what Bloom meant by 1:1 tutoring
Differentiated learning refers to the tailored approach or method of instruction to meet the learning preferences of different students, while keeping the learning goals the same for all students. In this approach, teachers find unique teaching modalities and activities to assist learners to meet standardized goals.
Personalized learning is a strategy that tailors learning to each student’s strengths, needs, and interests. This includes allowing students to have a voice and choice in what, how, when, and where they learn. In this approach, the goal and outcome can be different from one student to another.
Adaptive learning refers to the technologies monitoring student progress, using data to modify instruction at any time. These strategies create a student experience that is modified based on a student’s performance and engagement with the course materials and relies on technology and data about student performance to adjust and respond with content and methodologies that develop a pathway to the student’s mastery of a particular learning objective. (Peng et.al. 2019)
Depending on where you get your definitions, the differences can be harder to explain. The bottom line is, their differences are small in terms of which is more effective than the others. So far, there is no clear winner. Each has their own limitations, and are slightly unique. So, what EdTech really needs is a methodology that incorporates the best of the terms. The research team of Peng et, al, came close. They were off to a great start in 2019 when they described “Personalized Adaptive Learning (PAL).”
PAL is defined as a technology-empowered effective pedagogy which can adaptively adjust teaching strategies timely based on real-time monitored (enabled by smart technology) learners’ differences and changes in individual characteristics, individual performance, and personal development. This term noticeably leaves out individualized and differentiated learning, which gives some autonomy to the student, but doesn’t let them go off track, like they can in personalized learning.
To describe a style of learning that involves the best of each of these terms, and addresses the limitations, consider the word: Bespoke.
Fig. 1: Mapped comparison of Bespoke, Personalized, Adaptive Learning (dashed boxes); and Individualized, Differentiated Instruction methods (dotted areas) are mapped along the graph’s axis (X: Individual-Community) and (Y: Commonality-Personality)
In Figure 1, solutions deeply individual and highly personal (upper left quadrant) are known to be highly effective instruction, but costly due to the individualized 1:1 nature of instruction for enterprises. This quadrant reflects a tradition of taking the select top performers to providing advanced individualized instruction to advance their skills akin to a “top gun” academy for betterment. Solutions that are highly personal (“for me”) and strong on impacting community are highly scalable for the 100% of the workforce are represented by the upper right quadrant. Scalable implies large numbers, for a business that needs to impact a large population such as an enterprise workforce (high community) and have learning impacts that are highly individualized (high personality and highly individual) is the challenge that “Bespoke Learning” addresses. Bespoke learning delivers the high personality with BOTH individualized and scalable (high community) benefits, this is represented by the impact areas of the respective dashed boxes.
BESPOKE LEARNING IN EDUCATION TECHNOLOGY
Similar to the established technology powered PAL, Bespoke Learning is more than just personalized and unique to the learner. Each learner’s path is unique, even if the learning path were predetermined, the speed, pace and specific learning experiences are unique as they were developed with data about the learner. Motivation and engagement are increased. In contrast, the learner is given greater attention with Bespoke Learning. Autonomy and self-determination are the driving forces. Learners play a significant role in the process and aren’t simply bystanders, or ‘going along for the ride.” The methods and strategies are not static and they change as needed to meet the organization’s goals. Assessment is constant and ongoing. It is formative and summative at the same time. It’s structured and unstructured; whatever the learner needs. It’s like having a 1:1 teacher to student ratio, it’s learning that is “Just for Me”. A Bespoke Learning system changes every aspect of the learning experience, process and environment for the learner. Bespoke Learning raises the bar and sets it high enough to describe what education should be: An instructional approach that puts the learner first, regardless of age, ability, or eligibility. An approach where learners, not just students, learn just enough of what they need, at the right times for it to have a permanent change in behavior, and translates into success in their careers and daily lives, and into their futures. Learners deserve this approach. With the right tools and by incorporating learning theories, a Bespoke Learning system can involve both quality and quantity. While where isn’t a need for one more term to describe another learning approach for unique learners, what we do need is a term that raises the bar and combines the best of these terms. Bespoke Learning refers to a highly customized and tailored educational experience designed to meet the unique needs, preferences, and goals of the individual learner. The term bespoke combines each of the previous approaches into one overarching methodology. It doesn’t solve the issue of teacher bandwidth and data collection, that is what tools like Mentor126 is for, but it does describe the process of what education can be.
Here are the 6 criteria that set Bespoke apart:
1. Customized Learning Pathways: Bespoke Learning involves creating personalized learning pathways that cater specifically to the learner’s prior knowledge, skills, and learning goals. This means that the content, pace, and style of instruction are adjusted to fit the learner’s individual requirements rather than adhering to a one-size-fits-all curriculum.
Agentic AI can develop custom learning pathways to learning goals and dynamically adapt to changing conditions and priorities
2. Adaptive Process: In a bespoke online learning program, the content adapts dynamically based on the learner’s progress and performance. For example, if a learner excels in a particular area, the program might provide more advanced materials or skip over topics that have already been mastered. Conversely, if a learner struggles, the system may offer additional resources, explanations, or practice opportunities.
3. Personalized Feedback: Bespoke Learning ensures that feedback is tailored to the learner’s specific needs. This includes customized comments on assignments, targeted advice based on performance data, and personalized recommendations for improvement.
4. Learner Preferences and Goals: A bespoke online learning program takes into account the learner’s personal preferences, such as preferred learning styles (e.g., visual, auditory, kinesthetic) and career aspirations. This personalization might involve offering various types of content delivery, such as videos, readings, interactive simulations, or hands-on projects, aligned with the learner’s preferences. Goals of the learner and the enterprise are incorporated into a personalized knowledge graph, both factoring into the learner development and priorities.
Example relationship of a Learner’s personal knowledge graph (pKg) to knowledge
5. Learner Support and Resources: Bespoke Learning programs often provide tailored support and resources, such as one-on-one tutoring, mentorship, or access to specialized materials that align with the learner’s individual goals and challenges using the appropriate mentoring and coaching skills.
6. Flexible Scheduling: The program may offer flexibility in scheduling to accommodate the learner’s personal life, work commitments, or preferred learning pace, thus enhancing the learning experience by fitting into the learner’s unique lifestyle.
In essence, flexible scheduling with Bespoke Learning in a cloud connected online program, ensures that every aspect of the educational experience is customized to fit the individual learner’s needs, making learning more relevant, engaging, and effective. AI Assistants can facilitate scheduling, Intelligent AI can customize lessons to parse lessons on new learning based on an existing learner’s skill set, eliminating redundant training for an expert and gap-filling to assist a novice learner that is missing prerequisite knowledge. Intelligent assessments can measure learner knowledge to a set benchmark, similar to par in golf, and reinforce sub-par comprehension (gaps). Parsing can introduce concepts of micro-lessons that can be introduced throughout the day, or idle times such as during a commute. The net benefit is upskilling while reducing company downtime for work, one notion of upskilling in the flow of work.
With innovation and introduction of AI and specifically GenAI tools, the door is wide open to develop tools and establish claims for similar learning approaches to Bespoke.
SUMMARY
EdTech and online learning must deliver personalization with fidelity and validity—experiences that are repeatable, reputable, and effective. To create meaningful outcomes, learners need to reach a similar benchmark even if their pathways vary. Like a skilled human mentor, Agentic AI mentoring can take a multi-dimensional approach, addressing multiple competencies, such as product knowledge, and providing experiences tailored to individual strengths, weaknesses, filling gaps and enhancing soft skill needs. Data driven goal-based agents can tap into 1:1 individualized instruction and appeal to the leaner’s personal experiences, motivations, maximizing potential. Mentor126 introduces a revolutionary change in the system and changes the way people experience learning accelerating learning velocity.
At Mentor126, we believe learning must transcend simple personalization. It needs to deliver experiences that are rigorous, validated, and adaptable—so every learner reaches a shared benchmark, through diverse, tailored, dynamic pathways. Empowering people through upskilling demands a multi-dimensional approach that goes beyond mere technology; it requires a deep understanding of each learner’s strengths, gaps, and aspirations. The result? Meaningful progress that not only builds skills but fosters a resilient, agile workforce ready for the demands of tomorrow.
This mission calls for a shift in mindset. Companies must expand their focus from pedagogy to andragogy—and beyond that, to heutagogy, where learners drive their own paths toward mastery. While most EdTech solutions address isolated symptoms, true transformation lies in rethinking the system itself. By combining learning science, neuroscience, and andragogical principles with Agentic AI, Mentor126 is pioneering a new way forward—one that nurtures human potential at scale through deeply personalized, adaptive learning journeys all at scale, for the 100% of the workforce.
In this new era, technology’s role is not to replace human potential but to enhance it, providing people with the right tools to navigate their learning journeys with autonomy and purpose. Workers that can master their job and have the necessary skills will advance further. Mentor126 is empowering organizations to reimagine the learning experience, focusing on lifelong growth rather than quick fixes. We invite you to join us in transforming how people experience learning—unlocking potential, one personalized journey at a time.
This is Bespoke Learning, This is Mentor126.AI: “Just in Time, Just Enough, Just for Me.”
Learn & Read More
RESOURCES
Peng, H., Ma, S. & Spector, J.M. Personalized adaptive learning: an emerging pedagogical approach enabled by a smart learning environment. Smart Learn. Environ. 6, 9 (2019). https://doi.org/10.1186/s40561-019-0089-y
Computerized Adaptive Testing (CAT), is mainly based on simple rules or standards (Weiss and Kingsbury 1984)
References https://slejournal.springeropen.com/articles/10.1186/s40561-019-0089-y#Bib1
S. Adams Becker, M. Cummins, A. Davis, A. Freeman, C. Hall Giesinger, V. Ananthanarayanan, NMC Horizon Report: 2017 Higher Education Edition (The New Media Consortium, Austin, 2017)
E. K. Adu, & D. C. C. Poo (2014). Smart Learning: A New Paradigm of Learning in the Smart Age. http://www.cdtl.nus.edu.sg/Tlhe/tlhe2014/abstracts/aduek.pdf. Accessed 01 June 2019
L.W. Anderson, D.R. Krathwohl, B.S. Bloom, A Taxonomy for Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy of Educational Objectives: Allyn & Bacon (2001)
ASCD. (2018). Differentiated Instruction. http://www.ascd.org/research-a-topic/differentiated-instruction-resources.aspx. Accessed 01 June 2019
BECTA. (2007). Personalising Learning: The Opportunities Offered by Technology. http://archive.teachfind.com/becta/feandskills.becta.org.uk/display806e.html?resID=31571. Accessed 01 June 2019
C. Binder, Precision teaching: Measuring and attaining exemplary academic achievement. Youth Policy 10(7), 12–15 (1988)
DfES.(2006).2020 Vision: Report of the Teaching and Learning in 2020 Review Group. https://dera.ioe.ac.uk/6347/1/6856-DfES-Teaching%20and%20Learning.pdf. Accessed 01 June 2019
Dreambox. (n.d.) Personalized Learning. http://www.dreambox.com/personalized-learning. Accessed 01 June 2019
EDUCAUSE. (2016). Adaptive Learning Systems: Surviving the Storm. https://er.educause.edu/articles/2016/10/adaptive-learning-systems-surviving-the-storm. Accessed 01 June 2019
Green-Lerman, H. (2015). Visualizing Personalized Learning. https://www.knewton.com/resources/blog/adaptive-learning/visualizing-personalized-learning/. Accessed 01 June 2019
B. Gros, The design of smart educational environments. Smart. Learn. Environ. 3(1), 15 (2016)
T. Hey, S. Tansley, K. Tolle, The Fourth Paradigm: Data-Intensive Scientific Discovery (Microsoft research, Redmond, 2009)
R. Huang, J. Yang, Y. Hu, From digital to smart: The evolution and trends of learning environment. Open Educ. Res. 18(1), 75–84 (2012)
R. Huang, J. Yang, L. Zheng, The components and functions of smart learning environments for easy, engaged and effective learning. Int. J. Educ. Med. Technol. 7(1), 4–14 (2013)
INACOL. (2016). What Is Personalized Learning?. http://www.inacol.org/news/what-is-personalized-learning/. Accessed 1 June 2019
K-12 Education Team. (2014). Early Progress: Interim Research on Personalized Learning. http://k12education.gatesfoundation.org/resource/early-progress-interim-research-on-personalized-learning/. Accessed 1 June 2019
A.A. Kardan, M.A. Ebrahim, M.B. Imani, A new personalized learning path generation method: ACO-map. Indian J. Sci. Res. 5(1), 17 (2014)
Knewton. (2017). Knewton Adaptive Learning: Building the world’s most Powerful Education Recommendation Engine. https://www.knewton.com/wp-content/uploads/knewton-adaptive-learning-whitepaper.pdf. Accessed 01 June 2019
K. D. Li (2015). Personalized Learning in E-Learning Environment. http://www.docin.com/p-1199857000.html. Accessed 01 June 2019
S. J. Palmisano (2008). A smarter Planet: The Next Leadership Agenda. https://www.ibm.com/ibm/cioleadershipexchange/us/en/pdfs/SJP_Smarter_Planet.pdf. Accessed 01 June 2019
H.C. Peng, Z.T. Zhu, Measuring to assist learning: A Core mechanism of precision instruction in smarter education. E-Educ. Res. (3), 94–103 (2017)
J.M. Spector, Conceptualizing the emerging field of smart learning environments. Smart. Learn. Environ. 1(1), 2 (2014)
H.M. Sun, P.F. Liu, Learning activity in view of activity theory. China. Educ. Technol. 8, 29–35 (2015)
U.S. Department of Education, Office of Educational Technology Enhancing Teaching and Learning through Educational Data Mining and Learning Analytics: An Issue Brief (Washington, D.C, 2012)
U.S. Department of education, Office of Educational Technology, Transforming American Education: Learning Powered by Technology (Washington, D.C, 2010)
U.S. Department of Education, Office of Educational Technology, Reimagining the Role of Technology in Education: 2017 National Education Technology Plan Update (Washington, D.C, 2017)
H.P. Wan, D. Wang, Investigation on adaptive learning mechanism of big data based on Knewton platform. Mod. Educ. Technol. 26(5), 5–11 (2016)
J. K. Waters (2014). The Great Adaptive Learning Experiment. https://campustechnology.com/articles/2014/04/16/the-great-adaptive-learning-experiment.aspx. Accessed 01 June 2019
D.J. Weiss, G.G. Kingsbury, Application of computerized adaptive testing to educational problems. J. Educ. Meas. 21(4), 361–375 (1984)
N. Wleklinski (2017). Skinner’s Teaching Machine and Programmed Learning Theory. http://people.ischool.illinois.edu/~chip/projects/timeline/1954teaching_machine.html. Accessed 01 June 2019
G. Wu, Personalized education in globalization era. Nanjing J. Soc. Sci. (7), 104–110 (2015)
J. Yang, C. Hong, H. Yu, Kinshuk, Research focuses and trend on smart learning environments—Dialogue with ET&S editor Kinshuk. E-Educ. Res. 36(5), 85–88 (2015)
Z.T. Zhu, J.Q. Guan, The construction framework of “Network Learning Space for Everyone”. China. Educ. Technol. (10), 1–7 (2013)
Z.T. Zhu, H.C. Peng, Y.H. Li, Intelligence education: An approaches to smarter education. Open Educ. Res. 24(4), 13–24 (2018)
Z.T. Zhu, D.M. Shen, New paradigm of educational technology research based on Big Data. E-Educ. Res. (10), 5–13 (2013)
Z.T. Zhu, F. Wei, Educational Informatization 2.0: Starting on a journey of intelligence education guided by Smart Education. E-Educ. Res. 9, 5–16 (2018)
Z.T. Zhu, M.-H. Yu, P. Riezebos, A research framework of smart education. Smart. Learn. Environ. 3(1), 1–17 (2016)