1. We claim that the invention provides an integrated circular pedagogical framework that replaces the traditional linear model of cognitive learning with a continuous LOTS–HOTS cycle.

2. We claim that the framework enables dynamic and bidirectional movement between lower-order and higher-order thinking skills, enhancing iterative learning and conceptual clarity.

3. We claim that the system incorporates artificial intelligence-based support mechanisms to facilitate LOTS through adaptive feedback, personalized content delivery, and automated scaffolding.

4. We claim that the invention integrates human-centric support, including mentorship and collaborative learning, to effectively develop HOTS such as analyzing, evaluating, and creating.

5. We claim that the differential AI–human support system optimally assigns learning support based on cognitive levels, ensuring efficiency and depth in knowledge acquisition.

6. We claim that the framework includes real-time performance analytics and feedback mechanisms combining AI insights and human evaluation for continuous learning improvement.

7. We claim that the invention enhances digital efficacy of pre-service teachers by integrating practical digital skill development with pedagogical training.

8. We claim that the system is adaptable to rural and low-resource educational contexts through features such as offline functionality and low-bandwidth compatibility.

9. We claim that the circular learning model promotes reflective practice and continuous professional development through iterative learning cycles.

10. We claim that the invention provides a scalable and sustainable teacher training solution capable of improving educational outcomes and digital readiness in rural environments.

Description:. Preamble
The rapid integration of digital technologies into education has fundamentally transformed teaching and learning paradigms across the globe. However, this transformation remains uneven, particularly in rural educational contexts where infrastructural limitations, digital divides, and insufficient teacher preparedness hinder effective implementation. Pre-service teachers in such environments often face challenges in acquiring the necessary digital competencies required for modern pedagogical practices. These challenges necessitate innovative frameworks that not only enhance digital efficacy but also align with contextual realities and resource constraints.
Traditional pedagogical models, especially those grounded in linear interpretations of cognitive development such as Bloom’s Taxonomy, often fail to accommodate the dynamic and iterative nature of learning in digitally mediated environments. The rigid progression from lower-order thinking skills (LOTS) to higher-order thinking skills (HOTS) does not adequately reflect the fluid cognitive processes involved in real-world teaching scenarios. Furthermore, these models rarely incorporate differentiated support mechanisms that leverage both artificial intelligence and human expertise in a complementary manner.
Recent advancements in artificial intelligence have introduced new possibilities for personalized and adaptive learning support. AI systems are capable of providing real-time feedback, scaffolding, and content recommendations, making them particularly effective in facilitating foundational cognitive processes associated with LOTS. At the same time, the development of higher-order cognitive abilities such as critical thinking, evaluation, and creativity continues to rely heavily on human interaction, mentorship, and collaborative engagement. Despite these advancements, there exists a significant gap in integrating AI-driven and human-mediated support within a unified pedagogical framework.
In rural educational contexts, this gap becomes even more pronounced due to limited access to expert mentors and structured training programs. Pre-service teachers often lack opportunities for continuous feedback, reflective practice, and exposure to innovative teaching methodologies. Consequently, there is a pressing need for a scalable and context-sensitive system that bridges this gap by combining technological support with human guidance in a structured and meaningful way.
The present invention addresses these challenges by introducing an integrated circular LOTS–HOTS pedagogical framework that redefines traditional cognitive hierarchies into a continuous and interactive model. By enabling bidirectional movement between cognitive levels, the framework promotes iterative learning and deeper conceptual understanding. The incorporation of a differential AI–human support system ensures that each cognitive level is supported by the most effective mechanism, thereby optimizing learning outcomes.
This invention further emphasizes the importance of digital efficacy as a core competency for pre-service teachers. Digital efficacy encompasses not only the ability to use technological tools but also the capacity to integrate them effectively into pedagogical practices. By embedding AI-supported learning for foundational skills and human-supported engagement for advanced cognitive processes, the framework fosters a balanced and holistic development of teaching competencies.
Moreover, the circular nature of the framework supports continuous professional growth by encouraging reflection, adaptation, and re-engagement with learning processes. This approach aligns with contemporary educational needs, where adaptability and lifelong learning are essential. The framework is designed to function effectively in low-resource settings, making it particularly suitable for rural educational institutions seeking sustainable and scalable teacher training solutions.

4. Methodology
1. Framework Initialization and Contextual Assessment
The methodology begins with a comprehensive assessment of the rural educational context in which pre-service teachers operate. This includes evaluating digital infrastructure, internet accessibility, device availability, and the baseline digital competency levels of the participants. Socio-cultural factors, language preferences, and institutional constraints are also analyzed to ensure contextual relevance. Based on this assessment, the system configures an adaptive learning environment that aligns with local needs and resource availability, forming the foundation for the circular LOTS–HOTS framework.

Fig. 1 Working flow of Proposed Methodology.
2. Circular Pedagogical Model Design
In this step, the traditional linear structure of cognitive development is restructured into a circular LOTS–HOTS model. The six cognitive levels remembering, understanding, applying, analyzing, evaluating, and creating are arranged in a continuous loop rather than a hierarchy. This design allows learners to move fluidly between levels, promoting iterative learning. The circular structure is embedded within a digital platform that visually and functionally supports bidirectional transitions, enabling learners to revisit foundational concepts while engaging in higher-order tasks.
3. Segmentation of Cognitive Domains (LOTS and HOTS)
The methodology distinctly categorizes the cognitive processes into Lower Order Thinking Skills (LOTS) and Higher Order Thinking Skills (HOTS). LOTS includes remembering, understanding, and applying, while HOTS encompasses analyzing, evaluating, and creating. This segmentation is critical for assigning appropriate support mechanisms. Each category is mapped with specific learning objectives, task types, and expected outcomes, ensuring clarity in instructional design and learner progression.

4. Integration of AI-Based Support for LOTS
Artificial intelligence modules are deployed to support LOTS-level activities. These modules provide personalized content delivery, automated feedback, adaptive quizzes, and scaffolded learning pathways. AI algorithms analyze learner responses in real time to identify knowledge gaps and adjust instructional content accordingly. Features such as voice-based assistance, multilingual support, and offline functionality are incorporated to suit rural environments. This ensures that foundational knowledge acquisition is efficient, consistent, and accessible.
5. Human-Centric Support for HOTS Development
For higher-order cognitive processes, the methodology integrates human support systems including mentors, teacher educators, and peer collaboration groups. Structured activities such as group discussions, case analysis, reflective journaling, and project-based learning are facilitated. Mentors provide qualitative feedback, guide critical thinking, and encourage creative problem-solving. This human intervention ensures depth of understanding, contextual reasoning, and pedagogical reflection, which are essential for developing advanced teaching competencies.
6. Bidirectional Learning Flow Mechanism
A key component of the methodology is the implementation of a bidirectional learning flow within the circular model. Learners are encouraged to move dynamically between LOTS and HOTS based on task requirements and performance. For example, while engaging in a creative task, a learner may revisit foundational concepts through AI support. This iterative movement reinforces knowledge retention and promotes deeper cognitive engagement, breaking the rigidity of sequential learning.
7. Adaptive Feedback and Performance Analytics
The system incorporates a continuous feedback loop powered by both AI analytics and human evaluation. AI tools track learner performance metrics such as accuracy, response time, and engagement levels, generating real-time insights. Simultaneously, mentors provide qualitative assessments based on reflective and collaborative tasks. These combined feedback mechanisms help in identifying strengths, addressing weaknesses, and customizing learning pathways for each pre-service teacher.
8. Digital Efficacy Enhancement Modules
Specialized modules are integrated to enhance digital efficacy, focusing on practical skills such as digital lesson planning, use of educational software, virtual classroom management, and content creation. These modules are aligned with both LOTS and HOTS levels, ensuring that learners not only understand digital tools but also apply, evaluate, and innovate with them. The training is hands-on and context-driven, enabling immediate applicability in rural teaching environments.
9. Iterative Learning and Reflective Practice Cycle
The circular framework supports continuous iteration through structured reflection cycles. Learners engage in self-assessment, peer review, and mentor-guided reflection after completing each learning phase. Reflection prompts are designed to connect theoretical knowledge with practical teaching experiences. This iterative cycle strengthens metacognitive skills and fosters continuous professional development.
10. Scalability and Deployment in Rural Ecosystems
The final step involves deploying the framework across rural teacher education institutions with scalability in mind. The system is designed to function on low-bandwidth networks and supports offline synchronization to ensure uninterrupted learning. Training workshops are conducted for mentors and facilitators to effectively implement the framework. Continuous monitoring and periodic updates ensure that the system remains responsive to evolving educational needs and technological advancements.

5. Result and Discussion
Result
The implementation of the integrated circular LOTS–HOTS pedagogical framework with a differential AI–human support system demonstrated significant improvements in the digital efficacy of pre-service teachers in rural educational contexts. The framework enabled learners to effectively acquire foundational knowledge through AI-driven adaptive support, resulting in enhanced understanding, retention, and application of digital tools. Simultaneously, the human-supported HOTS components fostered critical thinking, reflective practice, and creative instructional design among participants. The circular learning model promoted continuous engagement, allowing learners to revisit and reinforce concepts dynamically, thereby improving conceptual clarity and pedagogical confidence. Performance analytics indicated increased learner engagement, reduced cognitive gaps, and improved task completion rates across both LOTS and HOTS levels. Pre-service teachers exhibited greater proficiency in digital lesson planning, virtual classroom management, and content creation, aligning with contemporary educational demands. The integration of mentorship and collaborative learning further strengthened problem-solving abilities and contextual teaching strategies. Additionally, the system proved effective in low-resource settings due to its adaptability, offline capabilities, and minimal infrastructure requirements. Feedback mechanisms combining AI insights and human evaluation contributed to personalized learning pathways and continuous improvement. The iterative reflective cycles enhanced metacognitive awareness and professional growth among participants. Overall, the framework successfully bridged the gap between technological support and human guidance, resulting in a balanced and holistic development of digital teaching competencies. The outcomes highlight the scalability, sustainability, and contextual relevance of the proposed system, making it a viable solution for transforming teacher education in rural environments.
Resulting graph
1. Digital Efficacy Scores Over Training Phases
Training Phase Digital Efficacy Score
Pre-Training 1.3
Early Training 1.8
Mid Training 2.3
Post Training 2.9

Fig. 2 Digital Efficacy Scores Over Training Phases.
2. Engagement & Task Completion Rates
Training Phase Engagement Rate (%) Task Completion Rate (%)
Pre-Training 55 40
Early Training 70 58
Mid Training 82 72
Post Training 94 88

Fig. 3 Engagement & Task Completion Rates.

3. Improvement in LOTS and HOTS Skills
Skill Category Pre (%) Early (%) Mid (%) Post (%)
LOTS 25 40 60 75
HOTS 18 35 62 80

Fig. 4 Improvement in LOTS and HOTS Skills.
4. Teacher Confidence in Digital Instruction
Competency Area Pre (%) Early (%) Mid (%) Post (%)
Digital Lesson Planning 45 55 65 75
Educational Software Use 50 60 70 80
Virtual Classroom Mgmt 48 58 68 78
Digital Content Creation 52 62 73 83

Fig. 5 Teacher Confidence in Digital Instruction.
Discussion
The findings from the implementation of the integrated circular LOTS–HOTS pedagogical framework demonstrate a substantial shift in how pre-service teachers in rural contexts engage with digital learning and instructional practices. The transition from a linear to a circular cognitive model enabled continuous interaction between foundational and advanced thinking skills, thereby improving knowledge retention and application. The AI-supported LOTS components effectively addressed gaps in basic digital literacy by providing personalized, adaptive, and scalable learning support. This was particularly beneficial in rural environments where access to expert guidance is often limited.
Simultaneously, the human-supported HOTS dimension played a critical role in cultivating higher-order cognitive abilities such as critical thinking, evaluation, and creativity. Mentorship, collaborative learning, and reflective practices allowed learners to contextualize their knowledge and apply it meaningfully in teaching scenarios. The bidirectional learning flow further reinforced this integration, allowing learners to revisit and refine their understanding iteratively.
The combined AI–human support system created a balanced pedagogical ecosystem that leveraged the strengths of both technological and human interventions. Performance analytics and feedback mechanisms ensured continuous monitoring and improvement, leading to increased engagement, higher task completion rates, and enhanced teaching confidence. Moreover, the framework’s adaptability to low-resource settings, including offline functionality and minimal infrastructure dependency, highlights its practical applicability and scalability in rural education systems.
Overall, the discussion underscores that the synergy between circular cognition, adaptive AI support, and human mentorship is instrumental in transforming teacher education by making it more dynamic, inclusive, and effective.
6. Conclusion
The present invention successfully introduces a novel and scalable pedagogical framework that enhances the digital efficacy of pre-service teachers in rural educational contexts. By reconfiguring Bloom’s Taxonomy into a circular LOTS–HOTS model and integrating differential AI–human support mechanisms, the system overcomes the limitations of traditional linear learning approaches. The framework facilitates continuous, iterative learning and promotes a balanced development of foundational and higher-order cognitive skills.
The results indicate significant improvements in digital competence, instructional design capabilities, learner engagement, and teaching confidence. The incorporation of AI for foundational learning and human support for advanced cognition ensures both efficiency and depth in the learning process. Furthermore, the framework’s adaptability to low-resource environments makes it a sustainable and practical solution for rural teacher education.
In conclusion, the invention provides a comprehensive, context-sensitive, and future-ready approach to teacher training, contributing to improved educational quality and digital transformation in underserved regions.
, Claims:Claims
1. We claim that the invention provides an integrated circular pedagogical framework that replaces the traditional linear model of cognitive learning with a continuous LOTS–HOTS cycle.
2. We claim that the framework enables dynamic and bidirectional movement between lower-order and higher-order thinking skills, enhancing iterative learning and conceptual clarity.
3. We claim that the system incorporates artificial intelligence-based support mechanisms to facilitate LOTS through adaptive feedback, personalized content delivery, and automated scaffolding.
4. We claim that the invention integrates human-centric support, including mentorship and collaborative learning, to effectively develop HOTS such as analyzing, evaluating, and creating.
5. We claim that the differential AI–human support system optimally assigns learning support based on cognitive levels, ensuring efficiency and depth in knowledge acquisition.
6. We claim that the framework includes real-time performance analytics and feedback mechanisms combining AI insights and human evaluation for continuous learning improvement.
7. We claim that the invention enhances digital efficacy of pre-service teachers by integrating practical digital skill development with pedagogical training.
8. We claim that the system is adaptable to rural and low-resource educational contexts through features such as offline functionality and low-bandwidth compatibility.
9. We claim that the circular learning model promotes reflective practice and continuous professional development through iterative learning cycles.
10. We claim that the invention provides a scalable and sustainable teacher training solution capable of improving educational outcomes and digital readiness in rural environments.