The Mnemonic-Integrated Neuroeducation Teaching and Learning Framework: A Brain-Informed Model for Meaningful Learning

Learning isn’t simply about remembering facts; it involves understanding, applying, reflecting on, and transferring knowledge to new situations. Yet many educational approaches continue to separate memory, thinking, and creativity, enabling students to recall information for examinations but often leaving them unable to apply it meaningfully beyond the classroom (Bransford et al., 2000).

This is an unpublished working draft developed as part of my doctoral research, and shared for professional feedback. Please don’t reproduce or distribute without the author’s permission.

Abstract

The MNEMONIC-INTEGRATED NEUROEDUCATIONAL TEACHING AND LEARNING FRAMEWORK is a proposed brain-informed conceptual framework developed as part of my ongoing doctoral research in Educational Psychology, specializing in Neuroeducation. 

Although mnemonics form a foundational component of the framework, it integrates mnemonic techniques with attention, meaningful encoding, retrieval, metacognition, and transfer, drawing on cognitive psychology, educational psychology, neuroscience, and learning sciences (Atkinson & Shiffrin, 1968; Flavell, 1979).

The framework is grounded in Dual Coding Theory (Paivio, 1971; 1986), Cognitive Load Theory (Sweller, 1988), Information Processing Theory (Atkinson & Shiffrin, 1968), Constructivist Learning Theory (Vygotsky, 1978), and Metacognition Theory (Flavell, 1979), while drawing on neuroeducation and learning-sciences research. 

Once fully developed and empirically validated, it’ll be shared through an open-source and copyleft philosophy, encouraging responsible adaptation, attribution, and collaborative development for meaningful learning in the age of AI. 

Keywords

Neuroeducation; Mnemonics; Educational Psychology; Cognitive Psychology; Neuroscience; Learning Sciences.

Introduction

Education is evolving rapidly as digital technologies, AI, and instant information transform learning and problem-solving. Success increasingly depends on understanding, critical thinking, and applying knowledge rather than memorizing facts (Bransford et al., 2000; Mishra & Koehler, 2006). Although mnemonic techniques effectively support learning and memory (Putnam, 2015), classrooms often prioritize memorization over understanding. Meaningful learning connects new knowledge with prior knowledge for retention and application, strengthened by curiosity and active engagement (Bransford et al., 2000; Vygotsky, 1978; Endres et al., 2024; Loewenstein, 1994; Gruber et al., 2014; Jirout & Klahr, 2012; Renninger & Hidi, 2016; Murayama, 2022; Deslauriers et al., 2019).

Mnemonics have evolved across neuroscience, digital learning, and special education (Mastropieri & Scruggs, 1998; Dunlosky et al., 2013), incorporating visual strategies, retrieval practice, and AI-supported personalization (Farrokh et al., 2021; Qu et al., 2024; Lee & Lan, 2023; Elabd et al., 2025; Lee et al., 2025; Saeidnia & Keshavarz, 2024; Yermekbayevna, 2025). Research supports meaningful encoding, retrieval, and retention (Bellezza, 1981; Higbee, 1979; Manalo, 2002), while appropriately designed mnemonics can address concerns about recall without understanding (Kilpatrick, 1985).

The proposed brain-informed conceptual framework integrates mnemonic techniques with meaningful encoding, associations, cognitive load, retrieval, reflection, and transfer (Paivio, 1986; Sweller, 1988; Atkinson & Shiffrin, 1968). It doesn’t advocate rote memorization, but positions memory as a foundation for meaningful learning.

History of Mnemonics

Mnemonic techniques have evolved over more than two millennia, with documented origins commonly traced to ancient Greece, where Simonides of Ceos developed the Method of Loci, using familiar locations to support systematic recall. 

Ancient India likewise developed sophisticated oral traditions for preserving the Vedas through rhythm, metre, repetition, and structured recitation, integrating memory, learning, and performance. Later reflected in works such as the Natya Shastra, these practices integrated memory, learning, and performance. 

During the Medieval and Renaissance periods, mnemonic techniques expanded into philosophy, religion, and education, using visual imagery and symbolic associations to support learning and knowledge organization. The Enlightenment brought more scientific approaches to memory, with Locke (1690) emphasizing meaningful understanding and logical associations, followed by Ebbinghaus’s systematic studies of memory and forgetting.

Throughout the twentieth century, cognitive psychology established the effectiveness of imagery, acronyms, keyword methods, and other mnemonic techniques. More recently, neuroscience, educational psychology, AI, and digital technologies have extended their application to encoding, retrieval, retention, and meaningful learning.

The Problem with Traditional Learning

Traditional education often emphasizes memorization, which may support recall but not necessarily deep understanding, long-term retention, or knowledge application (Bransford et al., 2000; Atkinson & Shiffrin, 1968).

With growing information overload and AI access, education must move beyond information acquisition toward meaningful knowledge, critical thinking, creativity, metacognition, and knowledge transfer (Henriksen et al., 2021; Luckin, 2018; Mishra et al., 2023).

The proposed framework addresses this integration gap by bringing together neuroscience, cognitive psychology, educational psychology, and learning sciences to support meaningful learning, durable retention, and transfer (Bransford et al., 2000; Tokuhama-Espinosa, 2014).

Literature Review

Research consistently demonstrates that mnemonic techniques enhance memory, learning, and long-term retention across educational contexts (Baddeley, 2000; Dunlosky et al., 2013; McCabe, 2011; McDaniel et al., 2014; Tokuhama-Espinosa, 2011; Zepeda et al., 2024). Dual Coding Theory, Cognitive Load Theory, Information Processing Theory, Constructivist Learning Theory, and Metacognition Theory explain how mnemonic techniques support meaningful encoding, retrieval, reflection, self-regulated learning, and knowledge transfer (Paivio, 1986; Sweller, 1988; Atkinson & Shiffrin, 1968; Flavell, 1979). AI further enables personalized and adaptive learning, complementing established cognitive and neuroeducational principles (Zawacki-Richter et al., 2019). Blunt and VanArsdall (2021) further found that animate imagery can improve memory performance when used with the Method of Loci, highlighting the role of imagery in supporting mnemonic encoding and recall. Tullis and Zhang (2026) found that generating mnemonics and retrieval practice improved memory and transfer compared with restudying, supporting the integration of retrieval practice after mnemonic construction.

The proposed brain-informed conceptual framework addresses this gap by integrating these principles into a coherent approach supporting meaningful learning, durable retention, and transfer. Recent scholarship reinforces the relevance of mnemonic techniques and educational neuroscience (Putnam, 2015; McCandliss, 2010).  It’s further informed by learning sciences research and foundational theories of meaningful learning, drawing on the works of Bartlett (1932), Ausubel (1968), Wittrock (1974, 1989), Hattie (2009), Ambrose et al. (2010), and Mayer (2021), which collectively strengthen its theoretical foundation.

Methodology

Findings from my doctoral research informed the development of the proposed brain-informed conceptual framework and provided preliminary empirical evidence on mnemonic techniques supporting part of the framework. Participation was voluntary, informed consent was obtained digitally, and responses were collected anonymously in accordance with BERA (2018) guidelines.

A quantitative, single-group pre-test–post-test design examined mnemonic techniques’ effectiveness in enhancing memory recall. A convenience sample of 100 learners — primary (37%), secondary (34%), and undergraduate (29%) — from six regions of India participated. Data were collected anonymously through Google Forms.

The intervention used three techniques: acronym-based, story-based, and visual memory activity. Participants recalled information from a passage on the Indus Valley Civilization before and after mnemonic instruction, including the acronym HAPPY MONKEYS DANCE SILLY TIGERS representing key concepts. The activity took approximately 10–15 minutes.

Descriptive statistics and a paired-samples t-test were used to compare pre- and post-test recall scores and assess statistical significance.

Findings and Analysis

A total of 100 learners from primary, secondary, and undergraduate educational levels across different regions of India participated in the research. All participants completed the pre-test, intervention, and post-test, resulting in a complete dataset. Tables 1–3 summarize participants by educational level, age group, and region.

Table 1

Distribution of Participants by Educational Level

EDUCATIONAL LEVEL	FREQUENCY	PERCENTAGE
Primary	37	37%
Secondary	34	34%
Undergraduate	29	29%
TOTAL	100	100%

Table 1 shows that participants included 37% primary, 34% secondary, and 29% undergraduate learners, providing representation across three educational levels. 

Table 2

Distribution of Participants by Age Group

AGE GROUP	FREQUENCY	PERCENTAGE
Below 10 Years	31	31%
10-14 Years	24	24%
15-18 Years	14	14%
Above 18 Years	31	31%
TOTAL	100	100%

Table 2 shows representation across four age groups, with participants below 10 years and above 18 years each comprising 31% of the sample. 

Table 3

Distribution of Participants by Region

REGION	FREQUENCY	PERCENTAGE
Central India	15	15%
East India	14	14%
North East India	8	8%
North India	12	12%
South India	15	15%
West India	36	36%
TOTAL	100	100%

Table 3 shows participation across six regions, with West India contributing the largest proportion (36%) and North East India the smallest (8%). 

Table 4

Comparison of Mean Pre-Test and Post-Test Recall Scores

STATISTIC	PRE-TEST RECALL SCORE	POST-TEST RECALL SCORE
Mean	3.34	4.19
Standard Deviation	1.01	0.81
Mean Difference	—	0.85
t(99)	—	7.76
p	—	<. 001

Table 4 shows an increase in mean recall from 3.34 (SD = 1.01) at pre-test to 4.19 (SD = 0.81) at post-test. The 0.85-point increase was statistically significant, t(99) = 7.76, p < .001, indicating improved recall following the mnemonic intervention. The reduction in standard deviation from 1.01 to 0.81 also indicates greater consistency in post-test scores.

Figure 1

Comparison of Mean Pre-Test and Post-Test Recall Scores

Figure 1 illustrates that the mean post-test recall score (M = 4.19) was higher than the mean pre-test score (M = 3.34), indicating improved recall following the mnemonic intervention. The standard deviation also decreased from 1.01 to 0.81, suggesting greater consistency in post-test scores. 

Table 5

Comparison of Pre-Test and Post-Test Mean Improvement Scores by Educational Level

EDUCATION LEVEL	MEAN IMPROVEMENT SCORE	STANDARD DEVIATION	PRE-TEST MEAN SCORE	POST-TEST MEAN SCORE
Primary	1.41	0.86	2.92	4.33
Secondary	0.68	1.07	3.61	4.29
Undergraduate	0.34	1.11	3.56	3.90
TOTAL	0.85	—	3.34	4.19

Table 5 shows improved recall across all educational levels, with the greatest gain among primary learners (M = 1.41, SD = 0.86; 2.92 to 4.33), followed by secondary learners (M = 0.68, SD = 1.07; 3.61 to 4.29) and undergraduate learners (M = 0.34, SD = 1.11; 3.56 to 3.90). Overall, mean recall increased from 3.34 to 4.19 following the mnemonic intervention. 

Figure 2

Comparison of Pre-Test and Post-Test Mean Improvement Scores by Educational Level

Figure 2 illustrates improved recall across all educational levels, with the greatest gain among primary learners (2.92 to 4.33), followed by secondary and undergraduate learners. This indicates that primary learners benefited most from the mnemonic intervention.

Figure 3

Comparison of Mnemonic Technique Effectiveness

Figure 3 illustrates the comparative effectiveness of the three mnemonic techniques. Visual memory activities achieved the highest score, followed by story-based and acronym-based techniques. This indicates that all three techniques positively supported memory retention, with visual mnemonics demonstrating the highest effectiveness.

Collectively, these findings provide empirical support for the use of brain-informed mnemonic techniques in enhancing memory recall across diverse educational settings.

Table 6

Empirical Findings from My Doctoral Research Providing Evidence Supporting the Development of the Framework in Brief

ASPECT	DETAILS
Research Design	Quantitative pre-test–post-test study
Participants	100 learners
Educational Levels	Primary, Secondary, and Undergraduate
Age Groups	Below 10 years, 10–14 years, 15–18 years, and above 18 years
Mnemonic Techniques	Acronym-Based, Story-Based, and Visual Memory Activity
Pre-Test Mean Score	3.34
Post-Test Mean Score	4.19
Overall Improvement	The mean recall score increased by 0.85 points
Highest Improvement by Educational Level	Primary learners recorded the greatest mean improvement of 1.41
Most Effective Mnemonic Technique	Visual Memory Activity achieved the highest mean score of 4.47

The findings presented are derived from ongoing doctoral research and are intended to illustrate the empirical foundation of the framework. Further analysis and validation will continue as the research progresses. One notable finding was that undergraduate learners showed smaller gains than primary learners, possibly reflecting greater reliance on established self-regulated learning strategies (Son et al., 2020). 

Novel Contribution 

The proposed brain-informed conceptual framework’s novelty lies in synthesizing established, evidence-based learning strategies into a coherent, brain-informed instructional sequence aligned with the cognitive processes of attention, meaningful encoding, association, mnemonic construction, retrieval, application, reflection, and transfer of learning. 

Unlike existing instructional models, which either omit mnemonic strategies or treat them as implicit within broader encoding processes, the framework establishes mnemonic construction as a distinct, explicit, and teachable stage, deliberately positioned between association, visualization, and retrieval practice.

The framework integrates these theories into a coherent, brain-informed learning pathway, clarifying how mnemonic techniques can support meaningful learning, durable retention, and knowledge transfer. 

Table 7

Theoretical Foundations of the Framework 

THEORY	FOCUS	LIMITATION	CONTRIBUTION
Dual Coding Theory	Verbal and visual encoding	Focuses mainly on encoding	Integrates encoding with retrieval and application
Cognitive Load Theory	Managing cognitive load	Limited to cognitive efficiency	Extends learning from attention to transfer
Information Processing Theory	Memory processing	Limited classroom guidance	Converts theory into a practical instructional sequence
Constructivist Learning Theory	Active knowledge construction	No structured memory framework	Combines active learning with memory strategies
Metacognition Theory	Self-regulated learning	Emphasizes monitoring	Embeds reflection throughout the learning process
The Mnemonic-Integrated Neuroeducational Teaching and Learning Framework	Integrated instructional conceptual framework	Requires further empirical validation	Unifies evidence-based principles into an eight-stage model for meaningful learning

The proposed brain-informed conceptual framework is developed through ongoing doctoral research. Its findings are preliminary and subject to further empirical validation. 

Overview of the Framework 

The proposed brain-informed conceptual framework integrates neuroscience, cognitive psychology, educational psychology, and learning sciences into a coherent approach to meaningful learning (Tokuhama-Espinosa, 2014). While mnemonics have been criticized for emphasizing recall over conceptual understanding (Kilpatrick, 1985), research highlights their value for memory, comprehension, and meaningful associations (Higbee, 1979; Manalo, 2002).

Recent developments have extended mnemonic use through visual strategies, retrieval practice, generative AI, and personalized mnemonic generation (Farrokh et al., 2021; Qu et al., 2024; Lee & Lan, 2023; Elabd et al., 2025; Lee et al., 2025). Building on these developments, the framework positions mnemonics within a broader process of attention, encoding, association, retrieval, application, reflection, and transfer (Atkinson & Shiffrin, 1968; Flavell, 1979), while recognizing the role of visuals, storytelling, and learning environments in supporting engagement, creativity, and knowledge construction (Mehta et al., 2019).

Comparison with Existing Instructional Models

The proposed brain-informed conceptual framework builds upon established educational theories and instructional models rather than replacing them. While many existing models focus on specific aspects of learning, the framework integrates evidence-based strategies into a single brain-informed framework that supports meaningful learning from attention and encoding to retrieval, reflection, and transfer.

Table 8

Comparison with Established Instructional Models Provides Context for the Proposed Framework

FRAMEWORK	YEAR	PRIMARY FOCUS	EXPLICIT MNEMONIC STAGE	NEUROEDUCATION INTEGRATION
Bloom’s Taxonomy	1956	Cognitive learning	No	No
Kolb’s Experiential Learning	1984	Learning through experience	No	Limited
Gagne’s Nine Events	1985	Instructional sequence	No	Limited
5E Instructional Model	1997	Inquiry-based learning	No	Limited
Understanding by Design	1998	Backward curriculum design	No	No
Merrill’s First Principles	2002	Problem-centred instruction	No	Limited
Rosenshine’s Principles	2012	Explicit instruction	No	Limited
Mnemonic-Integrated Neuroeducational Teaching and Learning Framework	2026	Brain-informed meaningful learning	Yes	Yes

The comparison highlights each model’s primary emphasis and is intended to situate the framework within the broader educational literature rather than to suggest that one model is superior to another.

Sequence 

Attention and Engagement → Meaningful Encoding → Association and Visualization → Mnemonic Construction → Retrieval Practice → Application → Reflection and Metacognition → Transfer of Learning

The sequence is progressive, with each stage building upon the previous one. Attention supports meaningful understanding; understanding strengthens memory, memory enables retrieval, and retrieval provides the foundation for application, reflection, and the transfer of learning.

The Eight Interconnected Stages

The learning sequence illustrates that meaningful learning is a continuous process, guiding learners from initial attention and meaningful understanding to long-term retention and the successful transfer of knowledge across diverse contexts.

Table 9

The Eight Interconnected Stages Supporting Meaningful Learning

NO.	STAGE	LEARNING JOURNEY	VISUAL CUE	ACTION
1	Attention and Engagement	SEE	👀	Activate curiosity and prior knowledge
2	Meaningful Encoding	UNDERSTAND	💡	Connect new learning to prior knowledge
3	Association and Visualization	CONENCT	🔗	Encourage visualization and associations
4	Mnemonic Construction	REMEMBER	🧠	Apply appropriate mnemonic techniques
5	Retrieval Practice	RECALL	🔄	Promote active recall and spaced practice
6	Application	USE	🛠️	Design authentic learning experiences
7	Reflection and Metacognition	REFLECT	🤔	Encourage reflection and self-assessment
8	Transfer of Learning	TRANSFER	🚀	Promote authentic transfer opportunities

The sequence represents a conceptual progression and shouldn’t be interpreted as a rigid or universally linear process. Educators may revisit earlier stages depending on learners’ needs.

The predominant goal is to promote meaningful learning by moving beyond rote memorization. Through a brain-informed learning process, the framework aims to foster deep understanding, long-term retention, critical thinking, creativity, and the successful transfer of knowledge across diverse contexts.

Illustrative Classroom Implementation

To illustrate the practical application of the proposed brain-informed conceptual framework, the following example demonstrates how the eight stages may be integrated into a classroom lesson. This example is intended to show how the framework can guide instructional planning and meaningful learning.

Table 10

The Application of the Framework Across Different Learning Contexts

Example 1: The Water Cycle

NO.	STAGE	LEARNING JOURNEY	VISUAL CUE	CLASSROOM ACTIVITY
1	Attention and Engagement	SEE	👀	Show a short video of rainfall and ask, “Where does rain come from?”
2	Meaningful Encoding	UNDERSTAND	💡	Explain evaporation, condensation, and precipitation using diagrams
3	Association and Visualization	CONNECT	🔗	Students draw the water cycle and relate it to everyday experiences
4	Mnemonic Construction	REMEMBER	🧠	Students create a mnemonic (e.g., ECPR: Every Cloud Produces Rain) for stages: Evaporation, Condensation, Precipitation, and Collection
5	Retrieval Practice	RECALL	🔄	Five-minute recall quiz without notes
6	Application	USE	🛠️	Students explain the water cycle in different climates
7	Reflection and Metacognition	REFLECT	🤔	Students write what strategy helped them remember best
8	Transfer of Learning	TRANSFER	🚀	Students connect the water cycle to weather, farming, or climate change

Example 2: Persuasive Writing

NO.	STAGE	LEARNING JOURNEY	VISUAL CUE	CLASSROOM ACTIVITY
1	Attention and Engagement	SEE	👀	Show an advertisement and ask, “What makes it persuasive?”
2	Meaningful Encoding	UNDERSTAND	💡	Explain the structure: claim, reasons, evidence, and conclusion
3	Association and Visualization	CONNECT	🔗	Create a mind map of the argument
4	Mnemonic Construction	REMEMBER	🧠	Use a mnemonic (e.g., CARE: Claim, Arguments, Reasons, Ending)
5	Retrieval Practice	RECALL	🔄	Recall the writing structure without notes
6	Application	USE	🛠️	Write a persuasive paragraph
7	Reflection and Metacognition	REFLECT	🤔	Reflect on which strategy improved writing most
8	Transfer of Learning	TRANSFER	🚀	Apply the structure to a new topic or subject

This example is provided for illustrative purposes to demonstrate how the framework may be applied in classroom practice. It’s not intended as empirical evidence of the framework’s effectiveness and should be adapted to suit different subjects, grade levels, and learning contexts.

The Scientific Foundations of the Framework

The proposed brain-informed conceptual framework is grounded in established theories explaining how people learn, remember, and apply knowledge. It synthesizes complementary perspectives from neuroscience, cognitive psychology, educational psychology, and the learning sciences into a coherent instructional model.

THEORY	THEORETICAL FOCUS	CONTRIBUTION TO THE FRAMEWORK
Dual Coding Theory (Paivio, 1971; 1986)	Verbal and visual representations	Supports meaningful encoding and retrieval
Cognitive Load Theory (Sweller, 1988; Sweller et al., 2011)	Working-memory efficiency	Supports meaningful organisation and cognitive efficiency
Information Processing Theory (Atkinson & Shiffrin, 1968; Roediger & Karpicke, 2006)	Encoding, storage, retrieval	Informs encoding, retrieval, and reinforcement
Constructivist Learning Theory (Vygotsky, 1978; Bruner, 1996; Sawyer, 2014)	Active knowledge construction	Supports association, exploration, and application
Metacognition Theory (Flavell, 1979; Schraw & Dennison, 1994; Son et al., 2020; Muis, 2007)	Monitoring and regulation	Supports reflection and self-regulated learning
Neuroeducation (Tokuhama-Espinosa, 2011, 2014; Howard-Jones, 2014; Petersen & Posner, 2012; Antony et al., 2017)	Brain–mind–education integration	Connects neuroscience with instructional practice

Together, these theories provide complementary foundations for the proposed brain-based conceptual framework, connecting attention, encoding, memory, active knowledge construction, metacognition, and transfer within a coherent learning pathway.

Applications of the Framework

The flexibility of the proposed brain-informed conceptual framework allows it to be adapted across a wide range of educational and professional settings.

Table 12

The Application Across a Range of Educational, Professional, and Training Environments

LEARNING CONTEXT	APPLICATION
School	Teaching science, mathematics, or languages through visual mnemonics, meaningful associations, and retrieval practice
University	Organizing complex concepts, theories, and research findings using evidence-informed mnemonic techniques
Corporate Training	Enhancing employee onboarding, professional development, and knowledge retention through structured learning techniques
Self-Learning	Supporting independent learners with spaced retrieval, self-testing, and reflective learning strategies
AI-Assisted Learning	Combining AI-generated explanations with active recall, critical thinking, and metacognitive reflection to promote meaningful learning

The applications presented are illustrative and demonstrate how the proposed brain-informed conceptual framework may be adapted across different educational and professional contexts. They’re intended as examples rather than an exhaustive list of potential applications. 

Educational and Pedagogical Implications

The proposed brain-based conceptual framework provides educators with a practical, evidence-informed approach to designing meaningful learning. By positioning mnemonic techniques within a broader process of attention, encoding, retrieval, reflection, and transfer, it moves beyond rote memorization toward deeper understanding, critical thinking, creativity, learner autonomy, and durable learning (Agarwal & Bain, 2019).

It can inform classroom instruction, curriculum design, teacher education, and professional learning, with applications across school, university, professional training, self-directed learning, and AI-assisted learning.

At the broader educational level, it supports evidence-informed, learner-centred approaches while offering a foundation for integrating AI, learning analytics, and personalised learning with established learning-science principles, maintaining focus on critical thinking, metacognition, adaptability, and transfer of knowledge.

Limitations

The proposed brain-informed conceptual framework is conceptual in nature and informed by findings from a single quantitative study involving a convenience sample of 100 learners across primary, secondary, and undergraduate educational levels in India. Accordingly, the findings should be interpreted with caution, and the framework requires further validation across larger, more diverse populations, educational settings, and cultural contexts.

The research didn’t examine the application of mnemonic techniques among neurodivergent learners, including individuals with ADHD, dyslexia, dyscalculia, dysgraphia, autism spectrum condition, developmental language disorder, intellectual disabilities, and other learning differences. Future studies should explore how the framework can be adapted to support diverse learner needs within inclusive educational settings.

Future research should also examine the suitability of mnemonic techniques across different types of learning content, learning objectives, and learner needs, as mnemonics may not be equally applicable to all learning situations.

Future Directions

The proposed brain-informed conceptual framework forms part of ongoing doctoral research and will evolve through empirical investigation, classroom implementation, interdisciplinary collaboration, and scholarly feedback. Future research will examine its application across diverse learners, subjects, and educational settings, including neurodivergent learners.

Further studies should use larger, diverse samples and longitudinal designs to examine long-term retention, while exploring integration with AI, adaptive learning, and learning analytics. Over time, the framework may evolve into a validated instructional model supporting evidence-informed curriculum, teaching, policy, and AI-enhanced learning.

Conclusion

Learning extends beyond memorization; it involves attention, meaning-making, association, retrieval, reflection, and transfer. Together, these processes support stronger memory, deeper understanding, adaptability, and problem-solving (Tokuhama-Espinosa, 2014).

The MNEMONIC-INTEGRATED NEUROEDUCATIONAL TEACHING AND LEARNING FRAMEWORK, a proposed brain-based conceptual framework, synthesizes neuroscience, cognitive psychology, educational psychology, and learning sciences into a coherent approach to meaningful teaching and learning. It provides a practical foundation for learner autonomy, creativity, and knowledge transfer in an increasingly AI-enabled world (Henriksen et al., 2021; UNESCO, 2023).

As an evolving conceptual framework, it invites continued empirical validation, interdisciplinary collaboration, and refinement to strengthen evidence-informed educational practice.

Contribute to the Development

If you’d like to contribute to the ongoing development of the proposed brain-informed conceptual framework, you’re invited to review this working draft and share your feedback through the accompanying feedback form. Your insights will play an important role in refining the framework and guiding its future development.

Acknowledgements

I gratefully acknowledge those educators, researchers, and professionals who may generously contribute their time and expertise by reviewing this working draft. Their constructive feedback will play an important role in refining and strengthening the framework. Any remaining limitations are the responsibility of the author.

Citation Notice

As this is an unpublished working draft, please don’t cite or reference this document without my prior permission. A formal citation will be provided following publication of the final version.

Document Information

Version: 0.9
Date: July 2026
Status: Shared for professional feedback

Licensing

The final version of the framework is intended to be released under an open-source and copyleft philosophy to encourage responsible sharing, adaptation, collaboration, and further research, while ensuring appropriate attribution to the original author.

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