Reimagining Flipped Learning via Bloom’s Taxonomy and Student–Teacher–GenAI Interactions

Abstract

This paper explores how generative artificial intelligence (GenAI) technologies, such as ChatGPT 4o and other AI-based conversational models, can be applied to flipped learning pedagogy to achieve enhanced learning outcomes for students. By applying Bloom’s taxonomy to intentionally align educational objectives to the key phases of flipped learning, our study proposes a model for assigning learning activities to pre-class, in-class, and post-class contexts that can be enhanced by the integration of GenAI. In the pre-class phase, GenAI tools can facilitate personalised content delivery, enabling students to grasp fundamental concepts at their own pace. During class, the interactions between students, teacher, and GenAI encourage collaborative learning and real-time feedback. Post-class activities utilise GenAI to reinforce knowledge, provide instant feedback, and support continuous learning through summarisation and content generation. Furthermore, our model articulates the synergies between the three key actors: interactions between students and teachers, learning support provided by GenAI to students, and use of GenAI by teachers to enhance their teaching strategies. These human–AI interactions fundamentally reshape the flipped learning experience, making it more adaptive, engaging, and supportive of the development of 21st-century skills such as critical thinking, collaboration, communication, and creativity.

1. Introduction

Flipped learning pedagogy has emerged as a practical approach to student-centred learning, offering a sharp contrast to the traditional teacher-centred model Bergmann and Sams (2012); Kwan et al. (2022). In flipped learning, the conventional roles of classroom time and homework are reversed. Students engage with instructional content, often online and before class, freeing up in-class time for interactive, hands-on activities that promote deeper understanding. This shift empowers students to take ownership of their learning, promoting critical thinking, collaboration, communication, and creativity.

The integration of educational technologies has significantly enriched the flipped learning pedagogy. The COVID-19 pandemic accelerated the transition to online learning, prompting educational institutions and governmental agencies to emphasise blended learning modes that combine face-to-face and online instruction Hill and Smith (2023); Wallace and Young (2010). Flipped learning aligns naturally with this approach: pre-class and post-class activities are primarily conducted online, allowing for self-directed learning, while in-class sessions are facilitated by teachers who guide and interact with students. This structure supports a dynamic learning environment where technology facilitates and enhances engagement and understanding.

The emergence of generative artificial intelligence (GenAI) represents a disruptive technological advancement that has already begun transforming teaching and learning since its public introduction by OpenAI in November 2022. GenAI differs from previous AI technologies in its ability to generate new content, simulate complex scenarios, and provide personalised feedback. Several important questions arise regarding the use of GenAI in education: In what contexts and ways can GenAI tools, such as ChatGPT, be most effectively integrated into teaching and learning? What role does Bloom’s taxonomy play in determining how to utilise GenAI in learning activities to achieve educational goals? Are teachers prepared to apply these disruptive technologies in their teaching to enhance student learning? Addressing these questions is crucial for maximising GenAI’s potential in education.

In the context of our study, GenAI offers capabilities that can enhance each stage of flipped learning. For example, in the pre-class phase, GenAI can generate personalised educational materials; design interactive, game-based activities; and provide formative assessments to students. In class, GenAI can support real-time feedback, facilitate group discussions with simulated scenarios, and create immersive learning experiences. Post class, GenAI can help students review and apply what they have learned through activities like summarisation, draft editing, and summative assessments.

In this paper, our contributions are twofold. First, by referencing knowledge and skill levels categorised by Bloom’s taxonomy, we describe a model for aligning both existing and new learning activities to pre-class, in-class, and post-class contexts that can be enhanced by the integration of GenAI. Second, based on this model, we explain how the dynamics of learning interaction (student–teacher), self-directed learning (student–GenAI), and teaching enhancement (teacher–GenAI) can be enriched, allowing educational experiences to be more effectively tailored to individual needs. In describing the model, we draw on our own experiences by offering practical insights for teachers considering the integration of GenAI into their teaching, regardless of whether or not they have previously applied flipped learning in their teaching practices.

The rest of this paper is organised as follows: Section 2 examines current technologies used in flipped learning and explores how GenAI can enhance the four pillars of this pedagogy. Section 3 presents the flipped pedagogy and GenAI (FPGA) model, which explains how the dynamics of various learning interactions, including student–teacher, self-directed learning, and teaching enhancement, can be enriched by GenAI. Additionally, the section outlines how Bloom’s taxonomy is applied to align GenAI-enhanced learning activities with the pre-class, in-class, and post-class phases, enabling personalised and effective educational opportunities to be created. Finally, Section 4 concludes the paper and outlines potential areas for future research.

2. Flipped Learning and the Role of Technologies

As an educational pedagogy, flipped learning has always benefited from innovations in technology to enhance the learning experience for students Kwan et al. (2024). The integration of technology into the three primary stages of flipped pedagogy—pre-class, in-class, and post-class—opens up opportunities to augment class preparation, facilitate self-directed post-class learning, and support group learning during in-class activities. The role of technology has been significant in the evolution of flipped pedagogy, especially in the era of AI and the digital revolution, through the use of online blended learning and other advancements.

In this section, we first provide an overview of the four pillars of flipped learning—flexible environments, learning culture, intentional content, and professional teachers that differentiate it from traditional teacher-centred approaches and support a student-centred educational model. Next, we explore current technologies utilised in teaching and learning activities within flipped pedagogy across pre-class, in-class, and post-class contexts. Finally, we discuss the emergence of GenAI and review some earlier work on enhancing the four pillars of flipped learning through the application of GenAI technologies.

2.1. Four Pillars of Flipped Learning

Flipped learning is built upon four fundamental pillars Flipped Learning Network (2025) that support a student-centred educational approach. These pillars are essential for transforming traditional classroom dynamics and utilising technological advancements to improve learning outcomes. Below, we will briefly explain each of these pillars:

Flexible Environment to Learn—Flipped learning should provide a variety of learning modes and resources, and learning space and time, and also match students’ learning pace. One of the core principles of flipped learning is providing a flexible environment that accommodates various learning modes and resources. This includes creating spaces where students can learn at their own pace and at the times that suit them best. According to Villegas (2022); Yarbro et al. (2014), flexibility in learning environments and schedules contributes to a more personalised and effective educational experience. The use of online resources and asynchronous learning tools enables students to engage with course materials and complete assignments beyond traditional classroom hours. This approach not only supports diverse learning needs but also promotes accessibility and adaptability in education.

Learning Culture—Flipped learning is an instructional approach that shifts the focus from passive content delivery to active student engagement. In this model, students are introduced to foundational concepts before class through structured activities such as readings or multimedia resources so they can engage with the materials at their own pace. These activities provide the necessary background for more complex discussions and problem solving during in-class sessions where learning is reinforced through application, collaboration, and interactive exercises. Subsequently, post-class activities serve to consolidate understanding by offering students opportunities for reflection and deeper application of concepts. Through restructuring the learning process in this manner, flipped learning encourages active participation and supports students in developing both fundamental knowledge and higher-order thinking skills Bergmann and Sams (2012); Kim et al. (2021); Lage et al. (2000).

Intentional Content—Teachers structure learning activities, course materials, and instructional processes to encourage students to engage with key concepts independently before class. This pedagogical approach enables classroom time to be used more effectively for student-centred and active learning strategies such as collaborative discussions, problem-solving exercises, and practical applications. A foundational aspect of this model is the development of high-quality pre-class materials, like instructional videos, formative assessments, and customised resources. The creation of these resources requires a significant investment of time and effort from teachers to ensure their effectiveness in supporting student learning Roach (2014); Walsh et al. (2021).

Professional Teacher—In a flipped classroom model, teachers play a pivotal role in providing personalised support to students, whether through one-on-one interactions, small group discussions, or whole-class engagement Rajaram (2019). Their responsibilities go beyond content delivery to include ongoing formative assessment, both by direct observation and systematic data collection, which informs instructional adjustments. This pedagogical approach enables teachers to tailor their teaching strategies to the changing needs of students, encouraging a learning environment that is both adaptive and student-centred. Furthermore, effective implementation of the flipped classroom requires teachers to engage in continuous professional reflection and collaboration with their colleagues. Continuous professional growth in this context relies on a willingness to incorporate feedback, explore innovative instructional methods, and cultivate a classroom culture that encourages exploration and experimentation Hall and DuFrene (2016); Zhou (2023).

2.2. Enhancing Flipped Learning with Technologies

In recent years, the integration of technology in education has expanded significantly, reshaping teaching and learning across various pedagogical frameworks Cueva and Inga (2022); Makarova and Makarova (2018); Tucker (2014). Learning management systems (LMSs), interactive content platforms, and AI-driven tools have been developed to support and enhance educational practices. These technological advancements have been particularly influential in the implementation of flipped learning models, where they facilitate the delivery of instructional materials, promote interactive engagement during class sessions, and enable more personalised learning experiences Huang et al. (2023); Sargent and Casey (2020).

The adoption of technologies in flipped learning has enabled teachers to deliver pre-class content through diverse media formats, such as video lectures, podcasts, and interactive simulations. This allows students to engage with the material at their own pace, outside the traditional classroom setting. In-class technologies, such as collaborative tools, real-time feedback systems, and educational software, have redefined the classroom experience, making it more interactive and focused on higher-order thinking skills. Furthermore, post-class technologies, including adaptive learning platforms and AI-driven assessment tools, offer personalised support and feedback, helping students consolidate their knowledge and skills.

2.2.1. Pre-Class Technologies

In the flipped learning model, the pre-class phase is designed to introduce students to new material before they engage in classroom activities. This initial exposure is often facilitated using digital resources, with instructional videos being among the most commonly utilised tools. These videos, either created by teachers or purchased from educational platforms such as Khan Academy or Coursera, enable students to engage with content at their own pace. The ability to pause, rewind, and review ensures that learners can revisit complex concepts as needed. Research has indicated that the use of video lectures can improve student engagement and comprehension, in particular when interactive features such as embedded quizzes or discussion prompts are included Deng and Gao (2023).

LMSs such as Moodle, Canvas, and Blackboard are integral to the organisation and delivery of pre-class materials. These platforms serve as a centralised repository where students can access readings, assignments, and other resources, facilitating their engagement with course content. In addition to content distribution, LMS platforms often incorporate analytics tools that enable teachers to monitor student progress and identify areas that require additional support. Studies have shown that the integration of an LMS in the pre-class phase contributes to improved student preparation and enhances students’ readiness for in-class learning activities Chen et al. (2023) and O’Flaherty and Phillips (2015).

2.2.2. In-Class Technologies

The in-class phase of the flipped learning model is dedicated to the application and reinforcement of material introduced during the pre-class phase. This stage often involves collaborative projects, discussions, and problem-solving activities that are designed to deepen students’ engagement with the subject matter. Technology plays an important role in supporting these activities. For instance, classroom response systems, including clickers and mobile-based applications such as Kahoot and Poll Everywhere, facilitate real-time feedback and active participation. These tools enable teachers to assess students’ understanding and make adjustments to the lesson as needed Phelps and Moro (2022).

Interactive whiteboards and smart boards are frequently utilised in flipped classrooms to enhance both the visual and interactive dimensions of instruction. These technologies enable teachers to present dynamic content, make real-time annotations, and actively involve students in the exploration and manipulation of information. Research suggests that the integration of such tools contributes to higher levels of student engagement and motivation by encouraging a more interactive and collaborative learning environment Shi et al. (2023).

The use of digital collaboration platforms, including Google Workspace, Microsoft Teams, and Padlet, plays a crucial role in facilitating group work and peer interaction, both of which are central to the flipped learning model. These platforms enable students to engage in real-time collaboration, share resources, and communicate effectively, regardless of geographical constraints. Studies have indicated that such technologies contribute to the improvement of group work dynamics and help cultivate a sense of academic community, both of which are fundamental to collaboration and learning Barbetta (2023) and Sawant (2021).

2.2.3. Post-Class Technologies

The post-class phase plays a crucial role in reinforcing and applying the knowledge acquired during both the pre-class and in-class activities. This phase typically involves reflection, consolidation of concepts, and further engagement with the subject matter. A range of technological tools are available to support these processes, which particularly emphasise assessment, feedback, and deeper exploration of course content. In addition, LMS platforms facilitate the administration of online quizzes and assignments, which serve as mechanisms for evaluating student comprehension and reinforcing key concepts. The automation of grading within these platforms not only streamlines the assessment process for teachers but also enables students to receive immediate feedback, thereby allowing them to identify areas for improvement and adjust their learning strategies accordingly Al-Fadda and Al-Yahya (2010) and Cevikbas and Kaiser (2022).

Beyond formal assessments, educational technologies that facilitate reflective learning are playing an increasing role in post-class activities. Platforms such as blogs, e-portfolios, and discussion forums provide students with opportunities to articulate their understanding, critically evaluate their learning experiences, and engage in meaningful intellectual discourse. Research suggests that these tools foster deeper learning by encouraging students to consolidate and apply newly acquired knowledge reflectively Howell (2021) and Kong (2014).

Furthermore, adaptive learning technologies, which tailor learning experiences based on individual student performance, are becoming more prevalent in the post-class phase. These systems use algorithms to provide personalised recommendations for further study, identify areas of weakness, and suggest targeted resources. Research has shown that adaptive learning can significantly improve student outcomes by providing a customised learning path that addresses the specific needs of each learner Clark et al. (2022).

2.3. Generative AI (GenAI) and Its Potential for Flipped Learning

GenAI, particularly models like ChatGPT, has revolutionised the landscape of AI since its introduction by OpenAI in November 2022 Wu et al. (2023). Unlike traditional AI, which focuses on specific tasks such as image recognition or predictive analytics, GenAI is designed to generate content—whether text, images, or music—by learning patterns from vast datasets through training. This capability allows GenAI to engage in conversations, generate essays, and create interactive content, as well as other applications, making it a transformative tool across multiple domains, including education.

In the context of education, GenAI signifies a departure from conventional educational technologies. Unlike traditional tools such as LMS or video-on-demand (VoD) platforms, which primarily facilitate the structured delivery of pre-existing content, GenAI possesses the ability to generate novel instructional material dynamically. This distinction underscores its transformative potential. While conventional educational technologies operate within predefined, linear frameworks designed for tasks such as automated grading and student data management, GenAI extends beyond mere automation by promoting creativity and adaptability. It enables the development of learning environments in which instructional content evolves in response to real-time student input. This shift represents a fundamental reconfiguration of pedagogical paradigms, situating GenAI as a pivotal force in shaping the future of teaching and learning Bozkurt (2023).

GenAI tools, such as OpenAI’s ChatGPT, Microsoft Copilot, Google Gemini, and DeepSeek, have the potential to enhance the four foundational pillars of flipped learning. By integrating these technologies into instructional practices, teachers may improve the efficiency of their teaching strategies while also facilitating more personalised and meaningful learning experiences for students. In this section, we examine current studies on the relationship between flipped learning and GenAI, noting a gap in research on how these approaches might work together to strengthen each phase of the flipped learning model. By exploring this intersection, we aim to outline a refined model that incorporates GenAI to enhance the flipped learning experience. This model will be discussed in detail later in the paper.

Integrating GenAI tools, such as ChatGPT, into flipped learning environments is increasingly seen as a way to enhance student engagement and improve learning outcomes. Recent studies reveal significant benefits; for example, the ChatGPT-based flipped learning guiding approach (ChatGPT-FLGA) has shown marked improvements in students’ academic performance, self-confidence, motivation, and creative thinking skills Li (2023). In another study, researchers found that AI-driven feedback in flipped classrooms helped students develop critical thinking and problem-solving abilities by offering immediate, personalised insights and resources tailored to each learner’s pace and needs Ray and Sikdar (2024). These findings indicate that the integration of AI-based support has the potential to create more individualised and adaptive learning experiences. By enhancing the flipped classroom model, AI tools can create a dynamic environment where students are encouraged to take greater responsibility for their learning while simultaneously developing essential skills for the future.

These studies demonstrate that ChatGPT is emerging as a valuable tool in higher education, particularly in developing critical, creative, and reflective thinking skills. The integration of AI into coursework not only enriches learning experiences but also promotes deeper engagement with course materials. Recent research suggests that students often exhibit improved critical thinking scores after having interacted with ChatGPT, highlighting AI’s capacity to facilitate more advanced analytical reasoning and comprehension Suriano et al. (2025). In engineering education, for example, the inclusion of ChatGPT in instructional design has been linked with significant improvements in student learning outcomes, underscoring AI’s potential to enhance interactivity and applied learning. These findings reinforce the perspective that, when integrated effectively, AI tools can serve as catalysts for deeper intellectual engagement and creative problem solving Zohuri and Mossavar-Rahmani (2024).

These discussions also extend to the role of AI in feedback mechanisms within online learning environments, particularly in flipped classrooms. Research has demonstrated that automated feedback systems can enhance student engagement and positively influence perceptions of learning by providing timely and structured responses. However, concerns remain regarding their effectiveness for high-achieving students who require more nuanced feedback. A study by Demszky et al. (2023) found that while AI-driven feedback reinforced fundamental concepts, it often lacked the sophistication necessary to support advanced learners in developing higher-order cognitive skills such as critical analysis and problem solving. This suggests that for AI tools to be truly effective in flipped learning environments, they must evolve to provide more personalised and in-depth feedback. A balanced approach, combining automated feedback with targeted instructional support, could significantly enhance the learning experience for students across different levels of proficiency.

These challenges highlight the existing gaps in the literature concerning the integration of GenAI into education, particularly within flipped learning environments. One understudied area is the practical difficulties teachers face when implementing these technologies Shailendra et al. (2024). For instance, further investigation is needed to determine how pedagogical frameworks must adapt to incorporate AI tools effectively and how assessment strategies should be refined to ensure academic integrity. While GenAI facilitates personalised learning experiences, concerns persist regarding potential over-reliance on AI, which could lead to a decline in critical thinking and ethical dilemmas related to academic integrity H.-P. H. Lee et al. (2025) and Zhai et al. (2024).

As flipped learning models continue to develop, examining both the benefits and limitations of GenAI remains essential for refining teaching methodologies Yan et al. (2024). Addressing these concerns will be critical in ensuring that AI technologies contribute meaningfully to student engagement, critical thinking, and overall educational outcomes. This paper seeks to advance discussions on how AI can be effectively integrated into flipped learning while maintaining rigorous academic standards and promoting deeper student involvement in their learning process.

3. Flipped Pedagogy and Generative AI (FPGA) Model

As previously discussed, GenAI tools have the potential to transform teaching and learning by offering personalised, efficient, and engaging experiences that extend beyond traditional educational approaches. Building on this foundation, we introduce the flipped pedagogy and GenAI (FPGA) model, which advances flipped learning through two key innovations. First, the model emphasises the dynamic interactions among student, teacher, and GenAI, thus enhancing instructional strategies, facilitating adaptive learning, and promoting essential 21st-century skills such as problem solving, collaboration, and digital literacy. Second, the FPGA model integrates Bloom’s Taxonomy into the flipped learning framework, ensuring that learning activities are systematically aligned across pre-class, in-class, and post-class phases. This structured alignment allows GenAI to personalise content delivery, support collaborative engagement, and reinforce learning outcomes, thereby enhancing the overall effectiveness of the flipped learning experience.

As an illustration of this approach, the proposed FPGA model is presented in Figure 1. In this model, the three vertices represent the primary stakeholders: students, teachers, and GenAI. The edges of the model depict the dynamic interactions and interconnected relationships among these stakeholders within the flipped learning framework. At the core of the model, concentric circles represent the alignment and distribution of GenAI-supported activities, instructional resources, and interactive learning experiences. These elements are mapped to Bloom’s taxonomy, ensuring coherence and continuity across the pre-class, in-class, and post-class phases of flipped learning.

3.1. The Student–Teacher–GenAI Collaboration Triangle

In this section, we will utilise the student–teacher–GenAI collaboration triangle to explain the role of GenAI in enhancing learning, supporting students, and improving teaching. By providing real-time feedback, automating assessments, and offering customised study resources, GenAI helps optimise learning before, during, and after class. Moreover, it contributes to reducing administrative workload, freeing teachers to focus more on student engagement and support.

3.1.1. Learning Interaction

Meaningful engagement between students and teachers is essential in promoting active participation in teaching, communication, and collaborative activities. Interaction in this context can be categorised into two primary forms: traditional and GenAI-assisted. Traditional interactions include face-to-face communication and the use of conventional educational technologies to facilitate student–teacher exchanges. However, the integration of GenAI tools has introduced new possibilities for enhancing these interactions, providing innovative means to engage students and personalise their learning experiences C. C. Lee and Low (2024); Suriano et al. (2025). By incorporating GenAI technologies, teachers can adapt their interactions to meet individual student needs, thereby improving both the efficiency and effectiveness of instructional engagement Borah et al. (2024).

The preparation of interactive quizzes, learning materials, and assessments often demands significant time and effort from teachers. GenAI has the potential to streamline these tasks by automating content creation and facilitating the development of customised learning materials Borah et al. (2024). The ability to generate tailored quizzes and assessments efficiently is particularly valuable for assessing students’ comprehension of key concepts before class, enabling teachers to offer targeted support where needed Foung et al. (2024). Furthermore, GenAI tools can automate the grading and evaluation of these assessments, a process that traditionally requires substantial manual effort. This not only alleviates the administrative burden on teachers but also ensures that students receive timely and consistent feedback. Consequently, the integration of GenAI in pre-class activities addresses many of the limitations associated with traditional methods, enhancing both instructional efficiency and student engagement.

GenAI can enhance in-class interaction between students and teachers in various ways. It can act as an intelligent assistant where students can consult GenAI during class when they have questions during class without interrupting the flow of the session. It can also create on-the-spot quizzes, polls, and other activities for quick knowledge checks that teachers can launch during the lesson to assess understanding in real time. GenAI can generate individualised follow-up questions or tasks based on students’ responses to initial questions. Additionally, teachers can create visual and contextual aids in class instantly for an explanation. Students and teachers can use the GenAI tools to take notes or summarise the in-class conversation so that students and staff can focus on discussion and engagement, knowing they will have access to accurate notes afterwards. Furthermore, GenAI can provide immediate, constructive feedback, guiding students on what they did well and where they can improve for in-class assignments or short answer questions. This type of real-time feedback allows students to adjust their approaches during class, promoting continuous improvement. Overall, the integration of GenAI in-class interactions makes classroom interactions more dynamic, responsive, and inclusive, helping students to remain engaged, receive immediate feedback, and deepen their understanding on the spot.

GenAI can play a significant role in interactions between students and teachers in post-class activities, primarily by reinforcing learning, clarifying doubts, and facilitating communication. GenAI tools can be used as “on-call” post-class tutors that can answer students’ questions about their confusion, explain concepts from the lesson, and guide students through challenging problems. This will help to support students to work independently while still having support available. GenAI can also provide personalised resource materials (videos, articles, or exercises) based on in-class performance and interaction to help reinforce learning. This approach promotes independent study tailored to individual needs. Additionally, teachers can use GenAI for performance analytics of post-class interactions, including the types of questions students frequently ask and their assignment performance. Teachers can use these insights to identify areas where the class as a whole may need extra review or support, improving the quality of future lessons. These activities illustrate how GenAI can be seamlessly integrated into various stages to support student–teacher learning interaction.

3.1.2. Learning Support

“Learning support” between students and GenAI emphasises the capacity of GenAI to provide tailored assistance and foster collaborative learning experiences. Within the flipped learning pedagogy, GenAI tools play an integral role in empowering students to independently explore resources that enhance their pre-class preparation, engage more effectively during class, and reinforce their learning post-class. By offering a broad range of resources and activities, GenAI supports students in actively constructing their knowledge and deepening their engagement with the course material across different phases of the learning process C. C. Lee and Low (2024) and Meli et al. (2024).

Students can utilise GenAI tools for pre-class preparation in various ways. For example, GenAI-generated study guides offer students a structured framework to identify key concepts and focus their attention on critical areas. In addition, GenAI-generated tutorials and explanations can provide additional layers of clarity, supporting deeper understanding. Through adaptive learning platforms, students can engage with customised exercises that address their individual learning needs, allowing for progression at their own pace. Moreover, the capacity to curate multimedia resources through GenAI allows students to engage with diverse content, thereby enhancing their overall learning experience. By providing access to varied formats such as videos, interactive simulations, and dynamically generated texts, these tools enable learners to explore complex topics from multiple perspectives, thereby encouraging deeper comprehension and engagement.

Moreover, within the classroom environment, GenAI-generated materials facilitate active participation by offering real-time support during discussions and collaborative activities. These tools enable students to retrieve relevant information as needed, ensuring they remain engaged and contribute meaningfully to academic discourse. The integration of GenAI-driven simulations, problem-solving platforms, and interactive question-and-answer systems encourages a more dynamic learning experience, reinforcing a student-centred approach in which GenAI serves as an assistive mechanism rather than a substitute for meaningful student interaction.

Beyond the classroom, GenAI continues to support students through customised follow-up activities. Resources such as review materials, quizzes, and additional practice questions provide opportunities for students to consolidate their understanding of key concepts. Furthermore, GenAI-powered self-assessment tools facilitate reflection and progress monitoring, allowing students to identify areas for further improvement. By offering ongoing academic support, these GenAI-driven resources contribute to a sustained learning process that extends beyond structured instructional settings, encouraging continuous engagement and intellectual growth.

The examples described above illustrate how GenAI can be seamlessly integrated into various stages of learning, providing students with valuable resources to support both their individual and collaborative learning experiences.

3.1.3. Teaching Enhancement

“Teaching enhancement” between teachers and GenAI shows how GenAI can support teachers by enhancing teaching methods, developing resources, and personalising student learning experiences and self-professional development Honig et al. (2024) and Meli et al. (2024). GenAI can be used to create adaptive teaching materials, deliver instant feedback, and streamline communication through virtual assistants, language translation, and interactive platforms. As an illustration, we discuss two possible teaching enhancements below.

Teaching Support—GenAI tools enable teachers to automate a range of tasks, allowing them to focus more on teaching rather than administrative duties C. C. Lee and Low (2024) and Meli et al. (2024). These tools assist in creating study guides, conducting GenAI-driven tutoring sessions, moderating discussion forums, and providing real-time feedback through automated grading systems. By using GenAI for grading quizzes, multiple-choice questions, and exams, teachers can save time and direct their energy towards more impactful teaching activities. GenAI technologies can facilitate the provision of targeted educational resources, including articles, videos, and exercises, enabling teachers to address the specific learning needs of individual students. For non-native speakers, these technologies offer valuable language support, such as text simplification and translation, ensuring that all learners can effectively engage with course materials Law (2024).

Resource Preparation—GenAI enables the creation of customised study materials that align with students’ levels of understanding Harjamäki et al. (2024). These materials may include adaptive quizzes and explanatory notes that evolve based on student progress. Beyond assessment tools, GenAI can generate a diverse range of pre-class resources, such as video lectures, audio explanations, and interactive learning modules. These resources introduce key concepts, propose discussion topics, and present real-world problems for students to examine before class Honig et al. (2024). Furthermore, GenAI assists teachers in developing structured lesson plans Karpouzis et al. (2024), integrating activity sequences, discussion prompts, and supplementary materials that correspond to specific learning objectives. This functionality reduces the time required for lesson preparation while enhancing instructional coherence.

GenAI technologies further contribute to the refinement of in-class activities Dickey and Bejarano (2023). Teachers can generate new educational content or modify existing materials in response to real-time student feedback Harjamäki et al. (2024). For instance, GenAI can produce additional case studies, analogies, or illustrative examples to clarify difficult concepts as students encounter challenges. Moreover, the technology supports the development of dynamic visualisations and simulations, including 3D models, that aid in conceptualising abstract phenomena such as chemical reactions or biological mechanisms. These tools enrich the learning experience by encouraging deeper engagement and understanding.

GenAI also enhances post-class learning by generating customised revision materials, such as mind maps, flashcards, and summarised notes Harjamäki et al. (2024). These resources can be tailored based on classroom discussions and individual student performance, reinforcing key concepts and supporting retention. On top of this, GenAI can design adaptive quizzes and practice exercises that respond to student progress, providing immediate feedback and personalised learning support. By integrating these capabilities, teachers can cultivate a more flexible and responsive educational environment that continuously adapts to student needs.

In conclusion, the incorporation of GenAI in flipped learning models offers significant pedagogical benefits. These technologies facilitate the efficient creation and adaptation of learning resources, allowing teachers to better accommodate diverse student needs. One of the primary challenges in flipped classrooms is the time-intensive nature of resource development. GenAI addresses this limitation by automating and streamlining content preparation. Consequently, teachers can allocate more time to interactive teaching and targeted student support, ultimately enhancing the overall learning experience.

This section outlines the dynamic interactions and interconnected relationships among the primary stakeholders, students, teachers, and GenAI, within the FPGA model, highlighting the collaborative and evolving nature of the learning environment.

3.2. Leveraging Bloom’s Taxonomy and GenAI in Flipped Learning

This section explores the roles of Bloom’s taxonomy in flipped pedagogy by structuring cognitive development and explores its adaptation in the FPGA model with GenAI to enable personalised learning experiences.

3.2.1. The Role of Bloom’s Taxonomy in Flipped Classroom Pedagogy

Bloom’s taxonomy, first introduced in the 1950s by Bloom et al. (1956) and subsequently revised by Anderson and Krathwohl (2001), provides a hierarchical framework for categorising cognitive skills. It progresses from basic knowledge acquisition to more complex forms of thinking, including analysis, evaluation, and creation.

Table 1. Bloom’s taxonomy domains.

Aspect (Role)	Cognitive (Knowledge)	Psychomotor (Skills)	Affective (Attitudes)
Purpose	Developing critical thinking and intellectual skills	Enhancing practical and technical competencies	Fostering emotional intelligence, empathy, and values
Focus	Knowledge acquisition and intellectual skills development	Physical skills, coordination, and hands-on proficiency	Emotional growth, attitudes, and value systems
Levels	Remembering, understanding, applying, analysing, evaluating, creating	Perception, set, guided response, mechanism, complex overt response, adaptation, origination	Receiving, responding, valuing, organising, characterising
Application	Academic learning, problem solving, decision making, research	Hands-on training, technical skill development, arts	Counselling, leadership development, team building, ethics training

outlines the three domains of Bloom’s taxonomy in education and training. The cognitive (knowledge) domain emphasises intellectual skills, understanding, and knowledge acquisition, while the psychomotor (skills) domain focuses on physical movement, coordination, and motor abilities. The affective (attitudes) domain addresses soft skills, behaviours, emotions, values, and self-regulation. Table 1 further details the purposes, focus areas, hierarchical levels, and applications within each domain.

This structured approach to learning has been widely adopted in educational settings to guide curriculum development and assessment design. This framework, widely applied in education, provides a structure to align educational activities with cognitive development, fostering more targeted and effective learning experiences. When applied to flipped learning, Bloom’s taxonomy can be utilised to guide the distribution of tasks to ensure that students build foundational knowledge outside of class, allowing in-class time to focus on deeper, interactive learning. A comparison of GenAI’s strengths and human strengths using the revised version of Bloom’s cognitive dimension is presented in Lynne (2023), where the author highlighted how humans and GenAI can support each other and also their roles at each level of Bloom’s taxonomy.

Pre-Class Activities and Lower Bloom Levels—Research has highlighted the role of pre-class activities in addressing the lower-order cognitive levels of Bloom’s taxonomy, such as remembering and understanding. These pre-class tasks often involve introductory resources, including recorded lectures, assigned readings, and short quizzes designed to convey foundational knowledge. According to Hung (2015), introducing students to new material through accessible, lower-complexity tasks prepares them to engage more actively in higher-order thinking during in-class sessions. Similarly, Abeysekera and Dawson (2015) emphasises that pre-class activities support self-paced learning, enabling students to engage with fundamental concepts before their application in classroom settings. This pedagogical approach aligns with Bloom’s taxonomy, which underscores knowledge acquisition as an essential foundation for the development of higher-order cognitive skills, such as analysis and synthesis. By allowing students to build a solid conceptual base before engaging in more complex tasks, pre-class activities contribute to more effective learning outcomes.

In-Class Activities and Higher Bloom Levels—Similarly, the flipped classroom model reconfigures in-class time to prioritise active, student-centred learning, with a particular focus on Bloom’s higher-order cognitive processes, including application and analysis. Jensen et al. (2015) provides empirical evidence of the benefits of this instructional shift, demonstrating that class time dedicated to collaborative exercises and problem-solving activities enables students to apply their foundational knowledge in more meaningful and contextually relevant ways. This structure allows teachers to design learning experiences that require students to analyse case studies, navigate complex problem scenarios, and engage in discussions that promote deeper conceptual understanding. Research by Lo and Hew (2017) further supports the effectiveness of this model, indicating that in-class activities aligned with the application and analysis stages of Bloom’s taxonomy promote critical thinking and enhance students’ ability to independently approach and resolve complex academic challenges.

Post-Class Activities Targeting Evaluation and Creation—For post-class tasks, Bloom’s taxonomy guides the design of activities that encourage students to reflect on and synthesise what they have learned. Post-class assignments often target Bloom’s top levels—evaluation and creation—by asking students to reflect critically on class material, draw connections, or create projects demonstrating their knowledge. Butt (2014) and Karabulut-Ilgu et al. (2018) emphasise that post-class tasks such as reflective writing, portfolio creation, and self-assessment not only consolidate learning but also encourage students to take ownership of their educational journey. This approach leverages the self-directed nature of post-class tasks, pushing students toward independent synthesis and original contributions.

3.2.2. Adapting Bloom’s Taxonomy for Personalisation with GenAI

Integrating GenAI within Bloom’s taxonomy in a flipped classroom setting opens new opportunities for personalising learning based on individual student needs. As Wong et al. (2024) and López-Villanueva et al. (2024) have suggested, GenAI can provide customised resources targeted at specific levels of Bloom’s framework, such as tailored pre-class summaries for foundational knowledge building or real-time feedback and simulations for in-class work. Through this approach, GenAI can adjust to reinforce basic skills for some students while offering more advanced challenges for others, leading to a personalised, flexible learning experience.

Human–GenAI collaboration is crucial for future education in various ways Al Yakin et al. (2024).

Table 2. Distribution of educational tasks between human teachers and GenAI-supported activities.

Bloom’s Domain	Human Educator	GenAI
Cognitive	Minor 1 to major	Minor to medium
Psychomotor	Medium 2 to major	Minor to medium
Affective	Major 3	Minor

¹ Minor refers to a limited involvement or contribution, providing basic or supplementary support. ² Medium refers to a moderate involvement, playing a significant but not dominant role. ³ Major refers to an extensive involvement, taking a leading and an essential role.

presents the involvement of human teachers and GenAI across Bloom’s three domains and also specifies the levels of involvement—minor, medium, and major—by describing the roles and their corresponding contributions and impact on activities in flipped learning. Human teachers play a significant role across all domains, with the most substantial impact in the affective domain. On the other hand, GenAI is most effective in the cognitive and psychomotor domains, with a limited role in the affective domain Zaphir et al. (2024). By assigning lower-order tasks, such as knowledge recall, to GenAI, teachers can focus on fostering higher-order skills like analysis, critical thinking, and creative problem solving.

The role of GenAI varies by discipline and learning objective. In technical fields such as software engineering, GenAI can enhance coding exercises, while in areas like law or medicine, human expertise remains central, with GenAI serving as a tool for background knowledge and context. One example is the use of GenAI to support students in laboratory-based activities by assisting in data analysis, pattern recognition, and predictive modelling Ariza et al. (2025) and Ngoc et al. (2023). Another example is the use of GenAI to aid in understanding human anatomy through 3D modelling and simulation, thereby improving spatial awareness and procedural skills Chheang et al. (2024). Similarly, in orthopaedic education and training, AI-driven simulations offer risk-free practice for motor skill refinement Gupta et al. (2024). These applications demonstrate GenAI’s role in enriching hands-on learning within classroom settings. The ultimate goal is to create a balanced educational environment where GenAI assists with repetitive, lower-order tasks, freeing teachers to focus on more complex, human-centred aspects of teaching. This task distribution, rooted in Bloom’s taxonomy, fosters a more engaging and effective learning experience within the flipped classroom model.

3.3. Aligning GenAI-Supported Activities in the FPGA Model

Aligning GenAI-supported activities in all three stages of flipped learning with Bloom’s taxonomy domains is crucial for the successful use of GenAI in the FPGA model. These activities are categorised according to Bloom’s framework but can be adapted or scaled depending on the subject, teacher, student cohort, and educational context.

Table 3. Mapping GenAI-supported pre-class activities to Bloom’s taxonomy level and domain.

Activity	Bloom’s Taxonomy Domain (Level)	Purpose	Example/Topic	GenAI Support
Content exploration	Cognitive (remember)	Introduce foundational knowledge	Summaries of key topics	Provide summaries, video explanations, and quizzes on specific concepts
Vocabulary building	Cognitive (understand)	Familiarise students with concepts	Definitions of flipped classroom terms	Create glossaries and visual aids to help students grasp terms and their context
Video reflection prompts	Affective (receive)	Stimulate engagement with content	Share impressions about a case study	Suggest reflection prompts and model example responses to guide initial thinking
Instructional videos	Affective (respond)	Motivate active viewing	Short videos on course concepts	Curate video playlists and generate guiding questions for students to answer
Hands-on tutorials	Psychomotor (perceive)	Build familiarity with skills/tools	Practice with GenAI-powered software	Create interactive simulations or personalised instructions to practice skills

outlines pre-class example activities mapped to Bloom’s taxonomy domains, their levels, and intended purposes, with the final column describing how GenAI can support these activities. In the pre-class phase, GenAI primarily focuses on building foundational knowledge, targeting lower-order Bloom’s levels in the cognitive domain and the initial levels of the affective and psychomotor domains.

In the in-class phase, activities focus on deeper understanding and application of concepts, leveraging higher-order cognitive processes and middle levels of the affective and psychomotor domains.

Table 4. Mapping GenAI-supported in-class activities to Bloom’s taxonomy level and domain.

Activity	Bloom’s Taxonomy Domain (Level)	Purpose	Example/Topic	GenAI Support
Group discussions	Affective (valuing)	Promote collaboration and empathy	Debating ethical GenAI use in education	Facilitate topic suggestions, summarisation of group discussions, and idea prompts
Problem-solving scenarios	Cognitive (apply)	Develop application of knowledge	Solving real-world flipped class problems	Provides case-based problems and step-by-step guidance to solve them
Concept mapping	Cognitive (analyse)	Connect different ideas	Create a concept map on flipped learning	Suggests structure and generating an initial draft of concept maps for refinement
Simulated role-playing	Psychomotor (mechanism)	Practice learned skills in context	Acting as a flipped classroom teacher	Provides real-time feedback and support simulations with adaptive scenarios
Guided peer feedback	Affective (organise)	Foster critical evaluation skills	Peer feedback on group presentations	Reviews and suggests feedback frameworks or improvement points for peer evaluations

presents in-class activities designed to deepen understanding and apply concepts, emphasising higher-order cognitive processes and engaging students at the middle levels of the affective and psychomotor domains. The table also explains how GenAI can support these activities.

The post-class phase includes activities that promote extended learning, self-assessment, and content creation, as illustrated in

Table 5. Mapping GenAI-supported post-class activities to Bloom’s taxonomy level and domain.

Activity	Bloom’s Taxonomy Domain (Level)	Purpose	Example/Topic	GenAI Support
Critical thinking essays	Cognitive (evaluate)	Foster deeper understanding	Reflect on the flipped model’s efficiency	Offers structured essay outlines, critiques drafts, and suggests refinements
Creative project creation	Cognitive (create)	Encourage innovation and synthesis	Develop a flipped classroom prototype	Assists in idea generation, project outlines, and creating visual prototypes
Reflective journaling	Affective (internalise)	Support self-awareness and growth	Journaling on flipped learning challenges	Prompts reflections and offers follow-up questions to deepen insight
Advanced skill application	Psychomotor (adaptation)	Demonstrate mastery of concepts	Build interactive complex project tasks (or flipped learning modules)	Provides adaptive templates and technical support for content creation
Data analysis activity	Cognitive (evaluate)	Assess and interpret outcomes	Analysing flipped learning effectiveness	Generates statistical insights, help with visualisations, and interprets results

. These tasks involve synthesis and evaluation, aligning with mid-to-higher Bloom’s levels across the cognitive, affective, and psychomotor domains. The final column illustrates how GenAI supports these post-class activities.

The FPGA approach ensures that GenAI activities are not only aligned with the flipped learning model but are also effectively scaffolded, addressing increasing cognitive complexity as students progress through the phases. Whereas examples were given to illustrate key activities, they can be tailored to specific educational disciplines. The integration of GenAI-supported activities throughout the flipped learning process enhances student engagement, personalises learning, and supports a deeper understanding of the content. This integration also strengthens the four pillars of flipped learning: flexible learning environment, learning culture, intentional content, and role of a professional educator.

4. Conclusions

In this paper, we have explored the transformative potential of GenAI technologies in enhancing flipped learning pedagogy. By aligning learning activities with the stages of flipped learning through Bloom’s taxonomy, the study introduces a structured model that integrates GenAI across pre-class, in-class, and post-class phases. The proposed FPGA model leverages GenAI to provide personalised content in the pre-class phase, facilitating foundational knowledge acquisition. In class, the model promotes collaborative learning and real-time feedback through dynamic interactions between students, teachers, and GenAI. Post-class, GenAI supports reinforcement, continuous learning, and mastery of concepts through instant feedback, summarisation, and content generation.

These contributions highlight the synergies among teachers, students, and GenAI, illustrating how such integration creates a more adaptive and engaging flipped learning environment. By emphasising critical 21st-century skills—critical thinking, communication, collaboration, and creativity—the study underscores how GenAI reshapes teaching and learning strategies in the flipped learning pedagogy. This model provides valuable insights for teachers and researchers seeking to integrate advanced GenAI tools into student-centred pedagogies, offering a foundation for further exploration and innovation in technology-enhanced education.

While this paper primarily focuses on developing a theory-based model as a foundational framework for future empirical research, illustrative examples—drawn from our previous work on flipped learning Kwan et al. (2024, 2022)—have been included throughout the manuscript to demonstrate its potential applicability across various contexts. These examples serve as a springboard for the next phase of our research, which will focus on implementing GenAI-supported activities in diverse teaching and learning settings to systematically assess their effectiveness. By collecting empirical data from both students and teachers, we aim to refine the model to better reflect evolving GenAI capabilities and shifting educational priorities. Particular attention will also be given to issues of accessibility and equity, ensuring that the model remains adaptable to a wide range of learning needs. This stepwise approach—from theory to illustration to application—will help ensure both the robustness and generalisability of the proposed model.

Subsequent research should focus on developing strategies to optimise the complementary roles of human teachers and GenAI in promoting student competencies across the cognitive, affective, and psychomotor domains outlined in Bloom’s taxonomy. This entails designing and empirically testing hybrid pedagogical models that integrate human instruction with GenAI, while accounting for contextual factors such as subject-specific requirements, institutional frameworks, and technological infrastructure.

Moreover, investigations should examine how the allocation of responsibilities between teachers and GenAI varies across disciplines and educational settings, particularly in relation to the distinct demands of the three learning domains. Understanding the influence of cultural, institutional, and contextual factors on this role distribution will provide critical insights into the development of inclusive, adaptable, and effective educational practices that fully leverage the potential of GenAI while maintaining the essential human element in teaching.