Catching the Learning Wave: How AI is Bridging the Science of Learning Gap in Professional Development

Mohsen Yahyaei

The contemporary corporate landscape demands a workforce that is not only skilled but also highly adaptable, capable of continuous learning and reskilling (EFMD Global, 2018). Effective professional development has, therefore, become a core strategic imperative. At the heart of designing effective learning experiences lies the Science of Learning (SoL)—an interdisciplinary fusion of cognitive psychology, neuroscience, and education that provides an evidence base on how people acquire, retain, and transfer knowledge. When applied well, SoL insights can improve speed-to-competence, reduce training costs, and lift on-the-job performance.

Despite this potential, a significant "research-to-practice gap" persists in corporate learning. Many organisations still rely on legacy methods that show little impact, often because research conducted in academic settings (Davidesco et al., 2021) is not easily translated to industry contexts.

Why SoL Matters for Organizational Growth

SoL principles, such as retrieval practice and cognitive load management, have demonstrated significant benefits. Retrieval practice alone can boost long-term retention (Agarwal et al., 2021), while managing cognitive load helps learners master complex procedures with fewer errors (Sweller, 2010). These gains are critical in safety-critical or innovation-driven sectors.

Furthermore, SoL aligns with established principles of adult learning (andragogy) (O’Neill, 2025; Darby, 2025), emphasising:

  • The Need to Know: Adults need to understand the relevance of learning.

  • Self-Direction: Adults prefer autonomy in their learning.

  • Life Experience: Adults use their existing knowledge as a foundation for new learning.

  • Readiness to Learn: Adults are motivated to learn what addresses their real-life challenges.

  • Problem-Centred Orientation: Adult learning focuses on solving specific problems.

  • Internal Motivation: Adults are driven by internal factors like job satisfaction and self-esteem.

Ignoring these principles contributes to the failure of many corporate training initiatives (Darby, 2025).

Barriers to Evidence-Informed Professional Learning

Several systemic hurdles prevent the widespread adoption of SoL in professional learning (Andreatta, 2024):

  1. Access to Evidence: Paywalled journals and jargon-heavy papers make research hard to access and digest (Parker, 2024).

  2. Time Constraints: Supervisors and Learning and Development teams often lack the time for deep design iterations due to production targets and focus on course completions.

  3. Incentive Misalignment: Promotion pathways often reward operational metrics over learning innovation.

  4. Initiative Fatigue: Concurrent organisational changes can cause well-intentioned learning pilots to lose momentum (Almond, 2024).

How AI Bridges the Research-to-Practice Gap: The NEXGROW Example

Artificial Intelligence (AI) offers a powerful means to translate SoL principles into scalable, personalised, and adaptive professional development solutions (Moe, 2025; Gupta, 2025). Platforms like NEXGROW (NEXGROW AI and NEXGROW Academy) are built on SoL principles to convert peer-reviewed insights into adaptive pathways that respect industry realities.

Key SoL Principles Operationalised by NEXGROW AI

NEXGROW Capability SoL Principle Operationalised Business Impact
Evidence Translation Engine Knowledge Mobilisation Curated "science snapshots" cut literature review time.
Adaptive Retrieval Boosters Retrieval Practice & Spaced Repetition (Agarwal et al., 2021; Hurix, 2025; Speach, 2025) Increases knowledge retention and reduces refresher training spend.
Cognitive Load Tuner Cognitive Load Theory
(Sweller, 2010)
Fewer learner errors during critical tasks suggest chunking or dual-coding.
Learning-to-Learn Micro-Course Self-Regulated Learning & Metacognition (Hurix, 2025) Shorter onboarding curves; cultivates skills like goal-setting and self-explanation.
Personalised Learning Journeys Self-Direction, Relevance & Meaningfulness, Active Learning (O’Neill, 2025; Hurix, 2025; Gupta, 2025) Tailor content to individual needs, job roles, and goals, increasing engagement and transfer.
Real-time Feedback & Adaptive Systems Feedback, Active Learning (Hurix, 2025) Provides immediate, personalized feedback; adjusts difficulty to keep learners optimally challenged.
Portable Data Integrations Engagement & Cognitive Load Analytics (Davidesco et al., 2021) Demonstrates ROI by correlating learning data with operational KPIs.

Concrete Strategies, Reimagined for Adult Learners with AI:

  • Retrieval Practice at the Coalface: Instead of end-of-module tests, AI can push micro-quizzes spaced over time, integrated into daily workflows (e.g., technicians receiving questions while logging work orders). This aligns with research on retrieval practice and spaced repetition (Agarwal et al., 2021; Speach, 2025).

  • Cognitive Load Alignment in Safety Drills: AI tools like a "Cognitive Load Tuner" can analyse VR scenarios or multimedia modules, recommending shorter sequences or better-timed cues to reduce mental effort and improve completion times, consistent with Cognitive Load Theory (Sweller, 2010).

  • Learning-to-Learn for Complex Roles: AI can support metacognitive "boot camps" that integrate goal-setting with retrieval and spacing, allowing employees to self-generate learning aids and achieve higher transfer task accuracy (Hurix, 2025).

Ethical and Practical Safeguards in AI-Driven Learning

The use of AI in learning introduces risks such as privacy breaches and algorithmic bias (Holmes & Porayska-Pomsta, 2022). Responsible platforms like NEXGROW address these through:

  • Data Minimisation & Opt-in Consent: Aligning with privacy standards like ISO 27701 and GDPR (Number Analytics, 2025).

  • Model Cards & Transparency: Plain language explanations of algorithm purpose, data sources, and limitations (Lee, 2025).

  • Human-in-the-Loop Dashboards: Enabling learning designers to override or refine AI suggestions.

  • Equity Audits: Periodically testing for disparate impact across demographic segments.

Practical implementation also requires seamless integration with existing systems (Human Resource Information System (HRIS), Learning Management Systems (LMS)), clear demonstration of ROI, and robust change management to build AI literacy (Almond, 2024).

Overcoming Barriers – A Roadmap for Leaders with AI-Powered Solutions:

Barrier NEXGROW-Enabled Solution
Limited Time Auto-curated evidence briefs and AI-guided design cut course-building cycles; deliver microlearning (Speach, 2025; Gupta, 2025).
Access & Jargon Natural language explanations translate research into actionable "design levers." (Andreatta, 2024)
Incentive Misalignment Dashboards integrate learning KPIS with operational targets; personalised paths align with individual/organisational goals (Gupta, 2025).
Initiative Fatigue Modular rollouts with engaging, adaptive, and demonstrably effective experiences prove ROI before expansion (Almond, 2024).

Conclusion: Turning Insight into Impact

Research unequivocally supports SoL strategies (Agarwal et al., 2021; Sweller, 2010). AI-powered platforms like NEXGROW provide the necessary infrastructure to embed these evidence-based principles seamlessly into adaptive learning journeys, making them effective in real-world corporate settings (Moe, 2025). By bridging the research-to-practice gap, organisations can move beyond seat-time metrics to demonstrable performance gains, future-proofing their workforce with the best the Science of Learning has to offer.

References

Agarwal, P. K., Nunes, L. D., & Blunt, J. R. (2021). Retrieval practice consistently benefits student learning: A systematic review. Educational Psychology Review, 33, 1409–1453. https://doi.org/10.1007/s10648-021-09595-9

Almond, A. (2024, December 4). 4 ways to combat initiative fatigue in education. Wipfli Insights. https://www.wipfli.com/insights/articles/edu-4-ways-to-combat-initiative-fatigue-in-education

Andreatta, B. (2024, December 9). Making science accessible: Overcoming barriers to the science of learning. Britt Andreatta. https://www.brittandreatta.com/making-science-accessible-overcoming-barriers-to-the-science-of-learning/

Darby, N., Why adult learning principles matter: A practical guide for better facilitation. TrainingPros Blog. Retrieved May 11, 2025, from https://blog.trainingpros.com/why-adult-learning-principles-matter-a-practical-guide-for-better-facilitation/

Davidesco, I., Matuk, C., Bevilacqua, D., Poeppel, D., & Dikker, S. (2021). Neuroscience research in the classroom: Portable brain technologies in education research. Educational Researcher, 50(9), 649–656. https://doi.org/10.3102/0013189X211031563

EFMD Global. (2018, December 14). Corporate learning in a VUCA world: A dictionary of the new learning language. EFMD Global. https://efmdglobal.org/wp-content/uploads/Corporate_Dictionary_EN_13.12.18_final.pdf

Gupta, D. (2025, April 16). AI in Learning & Development: What Leaders Need To Know. Whatfix Blog. https://whatfix.com/blog/ai-in-learning-and-development/Holmes, W., & Porayska-Pomsta, K. (2022). The ethics of artificial intelligence in education: Practices, challenges, and debates. Routledge.

Hurix. (2025). The Future of Enterprise Learning Trends Shape Corporate Training Programs. Hurix Digital. Retrieved May 11, 2025, from https://www.hurix.com/blogs/leveraging-the-science-of-learning-in-corporate-learning-programs/

Lee, S. (2025, March 27). 5 strategies for integrating AI ethics in classroom environments. Number Analytics Blog. https://www.numberanalytics.com/blog/classroom-ai-ethics-strategies

O’Neill, E., What is Adult Learning Theory?, LearnUpon Blog. Retrieved May 11, 2025, from https://www.learnupon.com/blog/adult-learning-theory/

Parker, S. (2024, February 22). Implementing the science of learning: Teacher experiences (CIS Research Report 47). The Centre for Independent Studies. https://www.cis.org.au/publication/implementing-the-science-of-learning-teacher-experiences/

Speach. (2025, April 29). Employees forget 90% of training within 1 week – here's how to fix it [Speach Method]. Speach Blog. https://speach.me/blog/employees-forget-90-of-training-within-1-week-heres-how-to-fix-it-speach-method

Sweller, J. (2010). Cognitive load theory: Recent theoretical advances. In J. L. Plass, R. Moreno, & R. Brünken (Eds.), Cognitive load theory (pp. 29–47). Cambridge University Press. https://doi.org/10.1017/CBO9780511844744.004 




Previous
Previous

From Classrooms to AI Coaches: Professional Development and Learning Evolution

Next
Next

Generative AI: A Game Changer for Corporate Training