CHAPTER 3

BLENDED PEDAGOGY 4.0: HUMAN TEACHERS AND

GENERATİVE AI İN THE CLASSROOM

Dr. Venkateswar Meher,

Srikanta Sahoo &

Kshetramani Bariha

1,2

Anchal Degree College, Padampur, Baragarh, Odisha, India.

Fakir Mohan University, Balasore, Odisha, India.

Email: venkatesmeher90@gmail.com;

ORCID: https://orcid.org/0000-0003-

2741-410X

Email: srikantasahoo2002@gmail.com

ORCID: https://orcid.org/0009-0001-

1045-12142

Email: kshetramanibariha14@gmail.com

ORCID: https://orcid.org/0009-0008-

9727-5647

Abstract

This paper explores conceptualizing Blended Pedagogy 4.0 as an integrative framework of the generative Artificial

Intelligence (AI) and positions human teachers in a corresponding partnership rather than a competitive relationship.

The rapid advancement of generative AI is restructuring current educational practices and teaching-learning, thereby

challenging a re-examination of traditional & blended pedagogical models. This paper critically examines the growing

roles of teachers as facilitators, proper decision makers, mentors, and guides, highlighting the irreplaceable emotional,

social, and moral dimensions of teaching that go beyond the algorithmic capabilities. The evolution of Pedagogy 1.0

to Pedagogy 4.0, the study says, is Blended Pedagogy 4.0 as a learner- centred, pedagogical flexibility, adaptive,

need-based, co-creative approach grounded in personalization and human judgment. The paper explores AI

supported to classroom practices, assessment mechanisms, and feedback model and the importance of AI instruction

needs to balance robotics with professional judgment to ensure authenticity and academic integrity. Generative AI is

investigated as a pedagogical tool that supports content generation, several assessments, timely feedback, and

instructional variation, while operating as a cognitive scaffold rather than a substitute for teachers. The paper critically

discussed the ethical, equity, & policy considerations, such as any bias, data privacy, transparency, and the digital

divide, with particular attention to teacher accountability in responsible AI use. Rooted in the Indian education

scenario, this study aligns the relevance of Blended Pedagogy 4.0 with the NEP-2020 vision, emphasizing its

application in multilingual, inclusive, and experiential learning and its ability to integrate Indian Knowledge

Systems. The reaved that the identifying key challenges and implications for redefining teacher competencies, future

research directions, advocating for a sustainable, ethical, and learned centred human AI partnership in education.

Keywords:

Blended Pedagogy 4.0; Generative Artificial Intelligence, Human AI Collaboration, AI-Enabled

Classrooms, Ethical AI in Education.

Introduction

Pedagogical innovation is of paramount importance in this age of technological development,

in which conventional and standardized methods of teaching and learning have proven ineffective

in holding learners' attention and catering to their learning needs. Innovative methods of teaching

and learning through AI technology have proven effective in promoting learners' engagement and

developing skills of critical thinking, creativity, and collaboration, which are of paramount

importance in the 21st century (Kong et al., 2024). AI-based teaching and learning methods

empower educators in managing administrative tasks and developing their skills (Kapoor et al.,

2023). The rapid emergence of Generative Artificial Intelligence (GenAI) has significantly affected

higher education (HE) and transformed the processes of teaching, learning, and research (Allison

et al., 2025). Generative AI applications have become more deeply embedded in learning contexts

and have introduced new possibilities for teaching and learning, although they have also raised

significant concerns about academic integrity, ethical evaluation, and information security (Baig &

Yadegaridehkordi, 2024; Chan, 2023; Lai & Tu, 2024). The appearance of generative artificial

intelligence cannot be divorced from the deep-seated needs of learners within the educational

process. If the educational content is related to the vital interests of learners, then generative

artificial intelligence will become a hot topic of concern for learners (Rudolph et al., 2024). Within

the educational process, the crisis awareness, curiosity, and knowledge thirst of learners are

infinitely magnified, and their concern for educational content will continue to increase. As soon

as the opposing educational information emerges, the learners will quickly become involved in the

discussions and debates on the educational content, displaying common trends and extreme

attributes within the educational phenomena (Hunt et al., 2024). The collective emotions were

mobilized, and the application of the generative artificial intelligence also displayed the attributes

of labeling and stigmatization. With the development of educational content and the continuous

enrichment of the applications of generative artificial intelligence, false information continues to

emerge, adding tension to educational discussions.

Shift from traditional and blended learning to Blended Pedagogy 4.0

In the fast-changing world. The use of technology along with classroom teaching is the key to

enabling learning in students (Woolfitt, 2015). Traditional learning is in-class learning, where the

teacher and the learner are face-to-face, as stated (Nortvig et al., 2018). Blended learning, as stated

by Garrison & Kanuka (2004), "is the thoughtful integration of classroom face-to-face learning

experiences with online learning experiences (Oliver et al., 2005). Blended learning is "the

integrated combination of traditional learning with web-based online approaches," where both

types of learning - online and classroom learning are considered. Over the years, the terms used to

describe blended learning have also evolved. Traditional learning involves direct instruction in a

classroom environment without much reliance on digital technology. Traditional learning has

remained the backbone of learning for many centuries, it may not always be appropriate for today’s

learners, who are increasingly familiar with technology and digital media in their daily lives (Kumari

& Murthy, 2024). The traditional learning environment has the use of direct interaction between

the teacher and students, which may be essential for discipline and community building among

students. However, the one-size-fits-all approach of the traditional method may not be effective

in catering to the learning needs of students. Blended Learning (BL) refers to the integration of

online and offline learning processes (Sharma, 2010; Kintu, M. J., Zhu, C., & Kagambe, E., 2017;

B. 2012). State that BL refers to the combination of online and face-to-face learning models

(Andrade & Coutinho, 2017). Blended learning is a pattern shift from the traditional method of

learning because it uses technology in the learning process. This is an adaptive and flexible learning

approach since it allows students to interact with the content, socialize, and take part in activities

both physically and online. The flexibility of blended learning addresses the needs of different

learners since it is a student-centered approach to learning (Kumari & Murthy, 2024).

The conventional pedagogical methods have encountered several challenges, especially

regarding their ability to meet the changing demands of the educational field. The conventional

methods have encountered challenges in ensuring that every learner gets personalized attention,

as they have not been very successful in meeting the needs of every learner. This is due to the

inability of conventional methods to cater to diverse learners, where some learners have lagged

behind, and others have gone too far (Guan et al., 2020; Sistemleri et al., 2021). The conventional

pedagogical methods have encountered several challenges, especially regarding technology.

Incorporating technology into conventional pedagogical methods is not very successful, as these

methods have failed to incorporate technology efficiently. This is due to the inability of

conventional methods to keep up with the changing technology, as technology is constantly

changing, and conventional methods have become less relevant (Guan et al., 2020). Incorporating

technology into conventional pedagogical methods is not very successful, as these methods have

failed to incorporate technology efficiently. The conventional pedagogical methods, especially

those enabled by technology, have encountered several challenges, especially regarding teacher

training programs. The teacher training programs have not been very successful, as conventional

pedagogical methods have not been very successful in enabling teachers to incorporate technology

into conventional pedagogical methods (Sistemleri et al., 2021).

Rationale for human–AI collaboration in classrooms

Student-teacher collaboration.

Learning partners: The students and teachers will be learning partners, and they will generate

knowledge and critically evaluate it based on the knowledge generated by AI. The teachers and

students will learn from each other based on the knowledge generated by AI. For instance, they

will learn together and critically evaluate the appropriateness of the learning videos and content

provided by AI-generated websites (Chinu. et al., 2025).

Navigators and guides:The teachers will be the navigators and guides, and they will ensure that

the students critically evaluate and validate the knowledge generated by AI. This is a critical step

in the learning process of students with AI (Chinu. et al., 2025).

Responsible Users and Ethical Guides: The teachers encourage the students to be ethical

guides in the use of artificial intelligence technology. They work along with the students to ensure

that they are being ethical in their use of artificial intelligence technology (Chinu. et al., 2025).

Motivators and Supporters: The teachers give emotional as well as intellectual support to the

students. They enhance the motivation levels of the students during the process of learning (Chinu.

et al., 2025).

Reflective learning: Reflective Learners: Students analyze themselves by using the feedback from

the AI technology to identify areas of improvement. Teachers analyze themselves using AI-

generated feedback to assess the effectiveness of their strategies (Chinu. et al., 2025).

Student-AI Collaboration

Information-AI collaboration: Information seekers and reviewers: Students actively seek

suggestions from AI, using it to plan and evaluate their learning. AI provides personalised

recommendations, which students critically review (Chinu. et al., 2025.

Self-learners and tutors: Students make use of AI in their improvement of learning. AI helps the

students in their improvement of learning since it offers them tutoring services (Chinu. et al., 2025).

Researchers and resource providers: Students make use of AI in their review of resources

offered by AI, such as articles, images, and videos. Students are able to learn new things and

develop their critical thinking skills (Chinu. et al., 2025).

Communicators and language assistants: Students make use of AI in reviewing their

communication skills, both in their native and learning languages. AI assists students in enhancing

their language skills through translation and vocabulary (Chinu. et al., 2025).

Experimenters and simulators: Students make use of AI in simulating experiments and in

critically reviewing situations. AI assists students in simulating situations, enhancing their capacity

for recognizing hallucinations and biases (Chinu. et al., 2025).

Teacher-AI Collaboration

Reviewers and facilitators: Teachers assess the interaction between the students and the AI

system and try to initiate a discussion on the risks, assumptions, and hallucinations associated with

it. This interaction can be a potential opportunity for the students and teachers to develop a better

understanding of each other (Chinu. et al., 2025).

Learning designers and enhancers: Teachers attempt to enhance the learning designs through

the resources and suggestions provided by the AI system. Such an approach may present an

opportunity for the teachers to enhance the learning goals for the students based on their various

interests (Chinu. et al., 2025).

Classroom organisers and managers: Teachers use the learning management systems provided

by the AI system to organise the classroom activities of the students. Such an approach may create

an opportunity to develop a structured learning environment (Chinu. et al., 2025).

Strategists and data analysts: Teachers use the data provided by the AI system to identify the

learning trends, strengths, and weaknesses of the students. Such an approach may create an

opportunity for the teachers to use the data provided by the AI system (Chinu. et al., 2025).

Conceptualizing Blended Pedagogy 4.0

The change from the 20th to the century is really big. Blended Pedagogy 4.0 is about education.

The old way of teaching was like a factory. It was about the teacher. Now Blended Pedagogy 4.0

is different. It is about the students (Mourtzis et al., 2023). We use computers and the internet to

learn. This is a change, for Blended Pedagogy 4.0. Pedagogy 1.0 refers to the last century, where

the tough and rigid teaching approach was adopted. There was no room for inclusiveness;

everyone was treated the same way. the adoption of technology and new teaching approaches has

significantly changed the education system. We are now in the era of new and innovative

technology such as AI. We have also moved beyond the Pedagogy 3.0 networks. We are now in

the AI-powered world of Pedagogy 4.0 (Rane et al., 2025). This is more than just technology. It

challenges the role of human beings in the Age of Information and Knowledge. We are no longer

just consumers of information. We are producers of critical thinking, social knowledge, and maker

knowledge. We can break down this evolution into six phases. Educators and theorists were

challenged by the “Man-Planet conflict” to build a borderless future for education. This future is

centered on human beings. It is important to understand the shift from “Industrial Schooling” in

the 20th century to “Smart Ecosystems” in 2025. Therefore, we will examine the technology, the

pedagogy, and the roles of teachers and learners in each phase.

Evolution of pedagogy: Pedagogy 1.0 to 4.0

The rapid pedagogy evolution that reflects continuous changes in teaching and learning

philosophies, influenced by social needs, technological advancement, and the development of

students’ potentialities (Bakar, 2021). This evolution from pedagogy 1.0 to pedagogy 4.0 shows a

shift from teacher-centred to learner-centred and from content transmission to ability

development.

Pedagogy 1.0

Students were considered passive learners at this time. Schools saw students as "empty vessels"

and filled them with facts. There was almost no space for curiosity or different ideas. Teachers

were in charge. They had all the knowledge. They taught us with lectures they made us memorize

things. They gave us standard tests. This is what some people (Altemueller, et al., 2017). There was

no scope for inclusiveness. Every student learned the same material at the same speed. It did not

matter what they liked or how they learned. The assessment system was too rigid and fixed. Written

exams measured learning by testing memory of facts. If we talk about the shortcomings and virtues

of this era, this system taught reading and math to many people. It also built a shared national

identity (Giordano, 2005).

Pedagogy 2.0

Pedagogy 2.0 refers to learner-centred, constructivist approaches, or to socialized or interactive

classrooms, and represents a shift from the old way of teaching, where the teacher is in charge, to

a greater focus on the learner. Letting them take part in the learning process. This is possible

because of the internet and new technology. This is about working together and sharing ideas. The

learners are not just listening; they are making things. Sharing them with others. The Pedagogy 2.0

is about letting learners take charge of their learning. They can use things like blogs and online

forums to do this (McLoughlin and Lee, 2008). This is based on the idea that we learn from talking

to each other and sharing our ideas. This is called the constructivist paradigm of learning

(Vygotsky, 1978). We learn from being connected to people on the internet (Siemens, 2005). This

is about thinking and working with others. It is also about learning all your life. These are skills to

have when you are using computers and the internet to learn. This is very important for people

who want to learn things and be good at using technology. The internet shifted from a site where

people viewed information to a site where people could share information. Websites like blogs,

wikis, YouTube, and social networks made it possible for anyone to share. Because of this,

education had to shift as well.

Pedagogy 3.0

Pedagogy 3.0 was born out of the use of information and communication technology in learning

and education. It has allowed for flexible learning through online platforms, Learning Management

Systems (LMS), and multimedia tools. It has also allowed students to move from receiving

knowledge to creating and sharing it (Matthew et al., 2021). Unlike earlier models of pedagogy,

Pedagogy 3.0 has allowed students to become co-creators of knowledge in networked and

technology-rich environments. It has allowed students to take charge of their learning and become

more creative and independent. Keats and Schmidt (2007) defined Pedagogy 3.0 as open learning

systems that facilitate global collaboration, idea generation, and knowledge sharing. This resonates

with Siemens (2005), who defined learning as a product of connections among people, networked,

and communities. It has also allowed students to make meaning and become lifelong learners. In

essence, Pedagogy 3.0 has allowed students to become critical thinkers and innovators, and has

prepared them for a knowledge-based and technology-overloaded society.

Blended Pedagogy 4.0

There are various definitions of blended teaching. The overall definition of blended teaching is

the comprehensive application of multiple modes of teaching and multiple teaching techniques. It

is not only a combination of online and offline learning, but also a combination of multiple learning

approaches, such as learning resources, learning media, learning environment, and learning style

(Zhang et al., 2022). In some cases, blended teaching and blended learning are used interchangeably

and refer to the same thing, and blended teaching and blended learning refer to slightly different

things (Bozkurt et al., 2020, 2022a). In most cases, blended teaching is defined as a combination

of offline and online teaching, and it has the features of both online and offline teaching so that

they are mutually complementary (Elgohary et al., 2022). More and more research has proven the

flexibility of blended learning. For example, in their research, Bozkurt and Sharma (2021) pointed

out that "the flexibility of blended teaching is reflected in that blended teaching can achieve full

play of the advantages of online teaching and offline teaching, and control the disadvantages, and

provide learners, teachers, and institutions with the flexibility of time, space, speed, and path”

(Bozkurt et al., 2020).

Defining Blended Pedagogy 4.0

Blended teaching can be roughly divided into the initial stage and the development stage. In the

early stages of blended learning, when the concept of blended learning was new, the main focus

of blended learning was the combination of face-to-face teaching (traditional teaching) and

computer-mediated (online teaching) activities as the main aspect of blended learning. In addition,

in 2000, the concept of "blended learning" mainly focused on the characteristics of blended

learning. Blended learning offers a better definition of the ratio of online and face-to-face teaching.

In addition, in blended learning, there are no limits to the combinations, as mentioned in the study

of Park and Doo (2024). In other words, what kind of blended learning will be implemented

depends on what is mixed. What’s the mix? How many teaching components are mixed and in

what order? According to Allen et al. (2007), blended learning can be divided into four types based

on the proportions of traditional learning, web-facilitated learning (less than 30%), blended

learning (30% to 79%), and most of the online learning (over 80%). In addition, future researchers

will often find that the ratio of blended learning in a hybrid course will be 30-70% online teaching.

On one hand, blended learning upholds the integrity of traditional learning and promotes the

development of online learning, mobile learning, and active learning (Xiang et al., 2022; Moskal et

al., 2013). With the development and application of blended learning, researchers have started to

explore and define blended learning from the perspective of teaching strategies, teaching methods,

and teaching design in the context of blended learning (Min W et al., 2023). Horn and Stolker

(2017) proposed four types of blended learning in the context of K-12 education, namely, rotating

model, flex model, self-mixing model, and rich virtual model. Education 4.0 is a new vision for

education that is driven by technological innovation and the changing needs of the workplace. In

Education 4.0, students are expected to develop critical thinking skills, problem-solving skills, and

creativity. They also need to be able to adapt to change and learn new things quickly (Van

Merriënboer & Kirschner, 2018; Gadicha et al., 2024)

Figure 1. Venn diagram of blended Learning

The implementation of GenAI in education is grounded in several theoretical frameworks, as

identified by W. Holmes (2021) and further developed by Slimi Z. Slimi (2023).

 Constructivist Learning Theory - Supporting personalized knowledge construction

 Adaptive Learning Systems - Enabling dynamic content adjustment

 Social Learning Theory - Facilitating collaborative learning environments

 Cognitive Load Theory - Optimizing information presentation

 Technology Acceptance Model - Understanding adoption patterns

Role of Human Teachers in AI-Enabled Classrooms

The various technologies associated with artificial intelligence are changing the nature of the

teaching profession by reducing administrative tasks, providing valuable insights, and facilitating

professional development. This has enabled teachers to channel their time and energy towards

creative and strategic elements of the profession, which enhances the quality of education imparted

(Fakhar et al., 2024).

Role of AI as an Assistant for Lesson Planning and Resource Allocation: The role of

artificial intelligence technologies becomes significant while discussing the effective planning of

lessons and allocation of resources for teachers. Technologies like Scribe Sense and Plan board

help teachers plan effective lessons through the application of artificial intelligence tech nologies

(Pedro et al., 2019).

These technologies help teachers analyze the curriculum and plan their lessons while suggesting

effective strategies for imparting knowledge through multimedia resources catering to different

learning objectives. This ensures effective allocation of time and resources for teachers while

planning their lessons (Jiménez-García et al., 2024). In addition, artificial intelligence technologies

like Classcraft help teachers monitor students' activities through learning analytics and allocate

resources based on the learning gaps for individual students. This enables teachers to allocate their

resources dynamically while enhancing their efficiency as effective teachers (Siddiqui et al. 2025).

Automating Grading and Assessment: The use of AI technology has significantly impacted

the grading and assessment process, which is considered one of the labor-intensive activities for

educators. AI technology, such as Gradescope, uses a machine learning algorithm in the grading

of assignments, quizzes, and essays. Not only does it save educators a great deal of time, but it also

helps them in individualized instruction (Hamid et al., 2022). For subjective assessments, AI

technology that incorporates NLP technology, such as Turnitin’s AI grading tool, can grade essays

based on grammar, coherence, and strength of arguments presented. AI technology in grading can

ensure timely feedback to students, which is considered to be essential to ensure student progress

in learning. AI technology in formative assessments can greatly enhance the teaching-learning

process since it provides educators an opportunity to conduct real-time quizzes, thus enabling

educators to adjust their strategies accordingly (Hamid et al., 2022; Siddiqui et al. 2025).

AI Tools for Continuous Faculty Development: AI tools play an important role in the

development of faculty members by providing personalized learning opportunities for educators.

For example, Coursera for Educators uses AI technology to offer personalized learning

opportunities for educators based on their styles of teaching. In addition, the tools offer faculty

members the opportunity to stay updated on the latest developments in the field of teaching by

providing updates on the latest trends and innovations in the field (Jiménez-García et al., 2024).

Coaching tools like Teach FX use AI technology to offer faculty members useful insights into how

they can effectively engage students in class. In addition, the tools offer faculty members the

opportunity to stay updated on the latest developments in their respective fields of study through

virtual mentoring tools (Hamid et al., 2022; Siddiqui et al. 2025).

The Balance Between Human Teachers and AI Systems: Even though there are different

efficiencies and personalizations in the field of education by applying AI technology, it is not

possible to replace the empathy, intuition, and comprehension of the teacher. It is imperative to

maintain a balance in the utilization of AI technology in the field of education. The main aim of

utilizing AI technology in the field of education is to assist the teacher (Jamal, 2023). Thus, the

utilization of AI technology in the field of education is not to replace the teacher. In the study

conducted by Jiménez-García et al., 2024, it is emphasized that "teachers are considered

fundamental in the development of students' emotional intelligence and critical thinking skills,

despite the use of AI tools for administrative and data-based tasks." In the study conducted by

Huang et al., 2021, it is emphasized that "it is imperative for teachers to acquire skills related to

digital teaching in order to use AI tools and maintain the 'human essence' of teaching." It is

imperative to provide training programs to the teachers in utilizing the AI system in such a manner

that it does not reflect a negative impact on the teacher-student relationship.

Role of Generative AI as a Pedagogical Tool

AI increases the efficiency of teachers through the automation of time-consuming activities like

curriculum alignment, content curation, and assessment mapping. Technologies like Scribe Sense,

Knewton, and Microsoft’s Copilot apply machine learning and natural language processing to

develop flexible and curriculum-aligned teaching plans using vast amounts of instructional content

data (Chatterjee & Bhattacharjee, 2020; Holmes et al., 2019). AI can assist teachers in automating

routine administrative tasks, including formatting, updating, and storing lesson file management,

which can save teachers more time to concentrate on teaching (Luckin et al., 2016; Schildkamp,

2022). precision in instruction is made possible by the role of AI, which can be seen in tools like

TeachFX and the AI Lesson Planner, which can provide teachers with analytics to help guide the

planning process (Roll & Wylie, 2016; Zhou et al., 2021). The recommendations may not take into

consideration contextual factors like classroom environment and student interests. Therefore, AI-

based recommendations should be used alongside teachers’ judgments. In this regard, teachers’

oversight should be viewed as crucial (Dalton & Proctor, 2021; Williamson & Eynon, 2020).

Artificial intelligence is also impacting instructional design by allowing for more structured,

adaptive, and data-driven instruction. Intelligent systems can help teachers with instructional

scaffolding and alignment to models like Bloom’s Taxonomy or Webb’s Depth of Knowledge to

provide cognitively coherent instruction (Holmes et al., 2019; Zawacki-Richter et al., 2019). AI

helps in differentiated learning by using student data to make appropriate recommendations. For

instance, Content Technologies Inc. and Bakpax are some AI-based platforms that offer dynamic

learning alternatives for different learner profiles without the need for teachers to prepare separate

plans (Kose & Ozturk, 2022). Using visual analytics in AI can provide instant feedback to detect

any gaps, inconsistencies, or information overload in the instructional design. This is similar to

agile principles in instructional design (Fischer et al., 2020; Ifenthaler & Yau, 2020). Despite these

possibilities of AI in instructional design, researchers emphasize that instructional designers must

consider cultural and pedagogical factors that go beyond the capabilities of AI. AI must act as co-

designers rather than replacing human designers (Aleven et al., 2018; Chounta & Avouris, 2019).

The extent of AI’s effect on pedagogy is largely dependent on how teachers view it as an

instrument. Majority of them view it as an aid in making processes easier and providing resources

aligned to curriculum needs. However, there is an ongoing debate regarding its alignment to

pedagogical needs as presented by AI-generated recommendations (Delgado et al., 2022; Molenaar

et al., 2021). For teachers who enjoy sufficient support in terms of training, infrastructure, and

flexibility of AI tools, it promotes feelings of confidence, creativity, and interest. However, in other

situations characterized by inadequate resources, it becomes overwhelming, particularly in

situations where there is dissonance between AI-generated results and teacher judgment

(Southgate et al., 2019; Suárez et al., 2023; Blaik-Hourani et al., 2022; Cheng et al., 2021).

Artificial Intelligence (AI) has now been recognized as a game-changer in the field of education,

especially in the context of "content generation." The potential of Artificial Intelligence to generate

learning and teaching contents such as lesson notes, quizzes, and summaries, among others, within

a matter of mere seconds is undeniable (Tang, 2024). Moreover, this is all possible due to the

sophisticated "Natural Language Processing" capabilities of Artificial Intelligence. One of the

greatest contributions of Artificial Intelligence to this field is its potential to enable "differentiated

learning." The traditional classroom setting often finds it difficult to cater to different needs,

talents, and learning styles within a single classroom. Artificial Intelligence has the potential to

analyze learner information and generate learning contents according to individual needs.

Classroom Practices under Blended Pedagogy 4.0

Blended Pedagogy 4.0 is the combination of conventional methodologies of teaching and the

latest digital technologies, especially Artificial Intelligence (AI), to create a unique, flexible, and

interactive learning experience for the students (Chene et al., 2020). Blended Pedagogy 4.0 is

consistent with the demands of 21st-century learning, which include competency-based learning,

critical thinking, and lifelong learning. Artificial Intelligence (AI) is a revolutionary change in the

instructional strategies of the classroom, as it enables teachers to adopt a more data-driven, flexible,

and student-centered approach to teaching. Artificial Intelligence enables teachers to create a

customized learning experience for the students by using the performance and learning patterns

of the students to create a unique learning experience for each student. The platforms of Khan

Academy and Duolingo are already using AI to create a customized experience for the students.

Blended Pedagogy 4.0 helps students collaborate by using both human and artificial intelligence

assistance. Facilitating Group Work with the Help of Artificial Intelligence Artificial intelligence

helps teachers create groups based on student capabilities. Smart Discussion Platforms Artificial

intelligence helps students discuss concepts by providing questions and ensuring all students are

heard. Language and Idea Support Artificial intelligence helps students’ express ideas clearly.

Enhancement of Peer Learning Artificial intelligence helps teachers provide feedback on student

collaboration.

Assessment and Feedback in Blended Pedagogy 4.0

The new form of teaching and learning, which has been referred to as Blended Pedagogy 4.0,

can be described as the combination of conventional teaching and learning strategies with the latest

technological tools, especially Artificial Intelligence (AI). In the new form of learning, different

approaches to teaching and learning with the help of AI have been of great importance in the

facilitation of learning for all. Another important aspect of Blended Pedagogy 4.0 is collaborative

learning with the help of Artificial Intelligence. Artificial Intelligence helps the students to

collaborate with one another by creating a balance of the number of students in the class (Pusca,

2024). Artificial Intelligence helps the students to communicate and collaborate with one another

by using digital platforms such as Google Classroom and Padlet, in which the students can share

ideas and collaborate with one another and discuss the ideas. Artificial Intelligence helps the

students to clearly state their ideas, especially for multilingual classes, and provide ideas on how to

improve collaborative learning among the students. learning activities can also be improved

through the integration of new learning experiences. For example, AI can be used to develop

discussion questions for critical thinking and learning. Simulation learning, with the support of

PhET Interactive Simulations, can also be used to ensure that the student learns complex learning

concepts through experiments and real-life situations. Formative assessments can also be

conducted through the use of instant feedback on student quizzes, assignments, and writing

activities, helping the student identify areas of strength and weakness. The use of chatbots can also

be used to provide learning support for the student outside the classroom. The gamification of

learning can also be used to promote learning through activities that adjust to the performance of

the student (). All these activities promote learning through engaging and effective learning

experiences, where AI is used as an assistant to the teacher (Rufino et al., 2025).

Ethical, Equity, and Policy Considerations

We are experiencing a period of fast development in educational technology, and the irrational

use of generative artificial intelligence has become a normal life in the educational field, which is

one of the biggest risks in the education sector today (Yao, 2024). Generative artificial intelligence

has grown up simultaneously with the education sector, and its irrational use can easily cause

complex and diverse educational problems, which are also mixed with various negative

information (Bukar et al., 2024). This not only influences the solving of educational problems and

the development and speed of educators' governance of educational issues but also threatens the

safety of education. Moreover, once a large number of irrational expressions appear on the

educational platform and form an educational group phenomenon, they will enhance educational

problems and even become the focus or catalyst of educational events (Daher et al., 2023).

Especially in the current era of highly developed information technology, educators, and learners

have considered educational platforms as important places for communication and learning. At

the initial stage of the outbreak of educational issues, there was a large number of irrational

comments concentrated on the platforms, which posed a serious challenge to the resolution and

disposal of educational issues. Not only does it affect the quality of communication on educational

platforms, but it also disrupts educational order, poses great obstacles to educational development,

and damages the credibility of educators (Dwivedi et al., 2023). Therefore, the problem of guiding

and regulating the irrational application of generative artificial intelligence in education has become

a serious practical problem that urgently needs to be solved (Yao, 2024; Siddiqui et al. 2025).

Indian Classroom Context and NEP-2020 Alignment

The concept of Blended Pedagogy 4.0 is also quite consistent with the vision of the National

Education Policy 2020 as it is focusing more on the development of classrooms in India that are

flexible and inclusive and also technology-enabled. The concept that is being implemented here is

that the blending of the digital technology and the pedagogy has to be done in order to enhance

the quality of education in India. The concept of Blended Pedagogy 4.0, as it is being implemented

in the classrooms in India, is the blending of traditional pedagogy and AI-based digital technology

like DIKSHA and Swayam. This is particularly significant in the classrooms of India because of

the diversification of classrooms in India, particularly in the rural and tribal regions of India. The

idea is to bridge the gaps in an inclusive manner. Blended Pedagogy, as it is used in the classrooms

of India in the vision of NEP-2020, is consistent with the change in the education system of India

because there is a shift in the education system of India from rote-based learning to critical

thinking, problem-solving, and overall development. One of the important features of the NEP-

2020 policy is that it has stressed the importance of the concept of multilingualism, inclusiveness,

and experiential learning, which may also be promoted through the application of Blended

Pedagogy 4.0. Furthermore, the policy has also stressed the importance of “learning through one’s

mother tongue/ regional language for better comprehension and learning at the foundational

level” (Government of India, 2020). The application of AI tools will also help in the effective

implementation of the concept of multilingual education, as it will provide facilities to enable the

active participation of the students. Furthermore, the policy has stressed the importance of

“learning through assistive technologies to promote inclusive education for the differently abled

students,” which may also be considered a constructivist learning theory, where the students will

be actively engaged in the learning process to develop the required knowledge for the subject of

interest.

Blended Pedagogy 4.0 will definitely provide an opportunity to incorporate Indian Knowledge

Systems in the curriculum as suggested in NEP-2020. This is because the NEP has emphasized

the need to incorporate India’s rich heritage of knowledge in different fields like traditional science,

arts, culture, etc., in modern education (Government of India, 2020). Digital media can be

effectively used to incorporate Indian Knowledge Systems in an interesting way like stories, etc.

For example, concepts like Ayurveda, Yoga, Environmental Studies, Crafts, etc., can be

incorporated in school education by using multimedia resources as well as by undertaking projects

in the community. This will not only help in preserving cultural heritage but also provide

interesting learning experiences to the students. Therefore, it can be said that if aligned with NEP-

2020, Blended Pedagogy 4.0 will provide an opportunity to develop an education system that is

neither too local nor too global but rather an education system that is suitable for India (Singh et

al., 2023; Mandavkar, 2025).

Challenges and Limitations

Although the utilization of Generative Artificial Intelligence in the field of education has

tremendous potential for transformation, the utilization of this technology in the field of education

is also associated with some challenges and limitations. This has also been noted by Chen et al.

(2022), wherein it has been stated that the utilization of GenAI in the field of education is

associated with some technical, educational, and ethical aspects that have to be considered. The

technical challenges associated with the utilization of GenAI in the field of education can be noted

in the aspect that the utilization of this technology in the field of education is associated with the

availability of appropriate technical infrastructure. This indicates that the utilization of GenAI in

the field of education is associated with the availability of appropriate technical infrastructure and

facilities. In some educational institutions, especially in developing nations, the availability of

appropriate technical infrastructure and facilities to support the utilization of AI technologies has

also been noted to be lacking. In addition, the utilization of GenAI in the field of education is also

associated with some technical complexities in the integration of the tools with the Learning

Management Systems (LMS) (Belkina et al., 2025). Another limitation is that there is a need for

educators to be trained and to have professional development to be adequately prepared to make

meaningful use of GenAI tools. Educators are not adequately prepared to make meaningful use

of GenAI tools. This could result in ineffective and shallow use of technology. Moreover,

educators need to have professional development to be adequately prepared to cope with the

dynamic nature of GenAI tools. Educators also need to be provided with technical support. In

addition to that, there is another limitation that is emerging regarding GenAI tools. The limitation

is that there is a tendency for educators and students to be over-dependent on GenAI tools. This

could result in a lack of critical thinking and creativity. Another limitation is that there is a need

for infrastructure and policy limitations to be addressed to enable GenAI tools to be used

adequately. Ethical concerns are another critical area of concern, and this is linked to the use of

AI in the education sector. The concerns of data privacy, bias, and equity of access are ethical

concerns associated with the use of AI in the education sector. The use of data in the AI system

can be a critical challenge to the privacy and security of the data, which needs to be well regulated.

In addition, the bias of the AI algorithm and the equity of access to the technology can exacerbate

the digital divide (Chen et al., 2022).

The arrival of educational phenomena has gradually weakened the "educational tools" of

educational platforms. Learners conceal their true identities, express and vent their emotions in an

unconstrained state, and even trample on their due learner responsibilities and morals. However,

educational platforms lack effective supervision of highly biased and extreme comments and

videos, resulting in irresponsible comments and videos repeatedly appearing on hot searches. As

a new battlefield for educational phenomena, educational platforms should review their published

content. However, due to the large scale and uneven quality of learners, the inadequate regulatory

system of education platforms, and the fact that education platforms use this to increase user

volume and activity to obtain economic benefits, there are many reasons for the "weak control"

of education platforms, which ultimately promotes the proliferation of educational phenomena

(Yao, 2024).

Future Directions and Implications

It is also imperative to adopt an outlook for its integration. The future directions should be

aimed at redefining teacher competencies, promoting human-AI partnerships, and filling the

research gaps for the successful integration of GenAI in the education sector. The implications of

GenAI in the Education Sector: One of the implications of the adoption and integration of GenAI

in the education sector is the redefinition of teacher competencies. In the changing digital

environment, the teacher is not considered an information provider but an information facilitator,

information designer, and information mediator. The teacher should develop competencies in AI

literacy, data analysis, and digital pedagogy for the successful integration of AI in the teaching-

learning process. Mishra & Koehler (2006) argued that the TPACK framework for teachers has

emphasized the need to develop the appropriate balance between technological knowledge,

pedagogical knowledge, and content knowledge. However, in the context of GenAI, the TPACK

framework should be extended to include the need for teachers to develop an understanding of

the ethics of AI, the capacity to evaluate the content generated by AI tools, and the capacity to

guide the student in the correct usage of AI tools.

The other major area of development is the way to human-AI partnership in the education

field. Instead of replacing the human teacher in this field, Gen AI can assist the teacher in his or

her role. The teacher can also provide his or her point of view in this case. The human-AI

partnership in this field can be named the “augmented intelligence” concept, as it is possible to

use the power of human as well as artificial intelligence in this case to achieve the best results.

However, it is also necessary to consider the way to human-AI partnership in terms of the problem

of overreliance on AI and human agency in the process of decision-making (Holmes et al., 2019).

Apart from this, the gap that has been identified in the further research area has also been identified

in the area of GenAI in the field of education. Even though the research that has been done up to

now has focused more on the efficiency and applicability of AI tools, the research that has been

done on the long-term impact of the application of AI tools on learning outcomes, cognitive, and

socio-emotional development is less. In addition to this, further research should also aim to

examine the applicability of GenAI tools in the context of the educational setting, particularly in

developing nations such as India, in which infrastructure and cultural aspects are taken into

consideration. In addition to this, further research should also aim to examine the aspects of

transparency, bias, and data, which are considered while implementing AI tools (Zawacki-Richter

et al., 2019).

Conclusion

In this ever-changing world of artificial intelligence-based learning and teaching, it has become

imperative to again focus on the importance of human teachers as the core catalysts for the process

of learning. While cutting-edge technologies such as Generative AI are being created to become

important tools for teachers and educators, these tools will not be able to replace the human

aspects of learning and teaching, such as empathy, ethics, culture, and inspiring and guiding the

learner. Teachers and educators will continue to be indispensable for facilitating critical thinking,

learning, and guiding the learner through this complex web of learning and society. The role of

teachers is not being replaced, but it is being redefined and reinforced in this new world of

intelligent technologies. It is in this regard that Blended Pedagogy 4.0 comes into the picture as a

balanced, ethical, and futuristic approach to learning and teaching. This approach to learning and

teaching strikes a wonderful balance between the conventional learning and teaching and the

innovative learning and teaching enabled by the AI technologies. This approach to learning and

teaching aligns with the values of equity, accountability, and integrity. This approach to learning

and teaching also aligns with the visions of learning and teaching that are currently being advocated

in the discourses of learning and teaching. Most importantly, this approach to learning and teaching

enables the building of a sustainable association between humans and AI in learning and teaching.

In conclusion, the future of education is not about the dichotomy of humans and machines; it is

about how we can create a harmonious synergy between the two. By positioning the teacher in the

center and using AI as a catalyst, Blended Pedagogy 4.0 provides a way to a future that is at the

same time technologically advanced, but also human, ethical, and sensitive to the needs of a world

that is in a state of change.

References

Aleven, V., Roll, I., McLaren, B. M., & Koedinger, K. R. (2018). Help helps, but only so much:

Research on help-seeking with intelligent tutoring systems. International Journal of Artificial

Intelligence in Education, 28(4), 593–618. https://doi.org/10.1007/s40593-017-0156-6

Allen, I. E., Seaman, J., & Garrett, R. (2007). Blending in: The extent and promise of blended education in

the United States. ERIC.

Allison, J., Hwang, G. J., Mayer, R. E., Pellas, N., Karnalim, O., de Freitas, S., ... & Sanusi, I.

(2025). From generative AI to extended reality: Multidisciplinary perspectives on the

challenges, opportunities, and future of educational computing. Journal of Educational

Computing Research, 63(6), 1327-1363.

Altemueller, L., & Lindquist, C. (2017). Flipped classroom instruction for inclusive

learning. British Journal of Special Education, 44(3), 341-358.

Andrade, M., &Coutinho, C. (2017). Implementing flipped classroom in blended learning

environments: A proposal based on the cognitive flexibility theory. Journal of Interactive

Learning Research, 28(2), 109.

Baig, M. I., & Yadegaridehkordi, E. (2024). ChatGPT in the higher education: A systematic

literature review and research challenges. International journal of educational research, 127,

102411.

Bakar, S. (2021). Investigating the dynamics of contemporary pedagogical approaches in higher

education through innovations, challenges, and paradigm shifts. Social Science

Chronicle, 1(1), 1-19.

Belkina, M., Daniel, S., Nikolic, S., Haque, R., Lyden, S., Neal, P., ... & Hassan, G. M. (2025).

Implementing generative AI (GenAI) in higher education: A systematic review of case

studies. Computers and Education: Artificial Intelligence, 8, 100407.

Blaik-Hourani, R., Rubaie, R. A., & Husseini, A. M. (2022). Educators’ perceptions of AI-based

lesson planning in under-resourced schools: A qualitative exploration. Technology, Pedagogy

and Education, 31(3), 343–359. https://doi.org/10.1080/1475939X.2022.2041239

Bozkurt, A. (2022). A retro perspective on blended/hybrid learning: Systematic review, mapping

and visualization of the scholarly landscape. Journal of Interactive Media in Education,

2022(1), 2. https://doi.org/10.5334/jime.655

Bozkurt, A., & Sharma, R. C. (2020). Education in normal, new normal, and next normal:

Observations from the past, insights from the present and projections for the future.

Asian Journal of Distance Education, 15(2), i–x.

Bukar, U. A., Sayeed, M. S., Razak, S. F. A., & others. (2024). Decision-making framework for the

utilization of generative artificial intelligence in education: A case study of ChatGPT. IEEE Access.

https://doi.org/10.1109/ACCESS.2024.

Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching

and learning. International journal of educational technology in higher education, 20(1), 38.

Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher

education: A quantitative analysis using structural equation modeling. Education and

Information

Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE access, 8,

75264-75278.

Chen, X., Xie, H., Zou, D., & Hwang, G. J. (2022). Application and theory gaps during the rise

of artificial intelligence in education. Computers and Education: Artificial Intelligence, 3,

100056. https://doi.org/10.1016/j.caeai.2022.100056

Cheng, X., Wang, M., & Liu, Y. (2021). Teachers’ attitudes toward AI in education: A case study

of smart lesson planning tools. British Journal of Educational Technology, 52(5), 2035–2050.

https://doi.org/10.1111/bjet.13113

Chinu, T. K. F., & Rospigliosi, P. A. (2025). Encouraging human-AI collaboration in interactive

learningenvironments. Routledge, vol.33, no. 2pp. 921-924

https://doi.org/10.1080/10494820.2025.2471199

Chounta, I. A., & Avouris, N. (2019). Orchestrating learning analytics awareness: Aligning p-

ISSN: 2828-8432; e-ISSN: 2828-8483, Hal. 192-202 tools and pedagogical goals. British

Journal of Educational Technology, 50(6), 3117–3134. https://doi.org/10.1111/bjet.12853

Daher, W., Diab, H., & Rayan, A. (2023). Artificial intelligence generative tools and conceptual knowledge

in problem solving in chemistry. Information, 14(7), 409.

https://doi.org/10.3390/info14070409

Dalton, B., & Proctor, C. P. (2021). Design for all learners: Universal Design for Learning.

Educational Psychologist, 56(3), 163–180. https://doi.org/10.1080/00461520.2021.1916499

Dede, C. (2014). The role of digital technologies in deeper learning. In Students at the Center: Deeper

Learning Research Series. Jobs for the Future.

Delgado, A., Wardle, F., & Tissenbaum, M. (2022). Teachers as designers: Exploring the use of

AI-powered tools for instructional planning. Computers in Human Behavior Reports, 8,

100216. https://doi.org/10.1016/j.chbr.2022.100216

Duangpummet, P., & Chenprakhon, P. (2021). Students’ perceptions of a designed online

asynchronous learning activity regarding the Community of Inquiry (CoI) framework. In

Proceedings of the conference (pp. 101–113).

Dwivedi, Y. K., Kshetri, N., Hughes, L., & others. (2023). what if ChatGPT wrote it?

Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational

AI for research, practice and policy. International Journal of Information Management, 71,

102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642

Elgohary, M., Palazzo, F. S., Breckwoldt, J., et al. (2022). Blended learning for accredited life

support courses – A systematic review. Resuscitation Plus, 10, 100240.

https://doi.org/10.1016/j.resplu.2022.100240

Fakhar, H., Lamrabet, M., Echantoufi, N., & Ajana, L. (2024). Towards a New Artificial

Intelligence-based Framework for Teachers’ Online Continuous Professional

Development Programs: Systematic Review. International Journal of Advanced Computer

Science & Applications, 15(4).

Fischer, C., Pardos, Z. A., & Baker, R. S. (2020). Mining big data in education: Affordances and

challenges. Review of Research in Education, 44(1), 130–165.

https://doi.org/10.3102/0091732X20903304

Gadicha, A. B., Gadicha, V. B., Sukhdan, K. K., & Bhattad, P. B. (2024). Blended Learning

Factors in Education 4.0: Application and Future Perspectives. In S. Chizwina & M.

Moyo (Eds.), Examining Information Literacy in Academic Libraries (pp. 231-239). IGI Global

Scientific Publishing. https://doi.org/10.4018/97 9-8-3693-1143-1.ch013

Giordano, G. (2005). How testing came to dominate American schools: The history of educational assessment.

Peter Lang.

Graham, C. R. (2006). Blended learning systems. In C. J. Bonk & C. R. Graham (Eds.), The

handbook of blended learning: Global perspectives, local designs (pp. 3–21). Pfeiffer.

Guan, C., Mou, J., & Jiang, Z. (2020). Artificial intelligence innovation in education: A twenty-

year data-driven historical analysis. International Journal of Innovation Studies, 4(4),

134–147. https://doi.org/10.1016/j.ijis.2020.09.001

Hamid, T., Chhabra, M., Ravulakollu, K., Singh, P., Dalal, S., & Dewan, R. (2022). A Review on

Artificial Intelligence in Orthopaedics. Proceedings of the 2022 9th International

Conference on Computing for Sustainable Global Development, INDIACom 2022,

365–369. https://doi.org/10.23919/INDIACom54597.2022.9763178

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications

for teaching and learning. Center for Curriculum Redesign.

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications

for teaching and learning. Center for Curriculum Redesign.

Hunt, R. A., Kurdoglu, R. S., & Lerner, D. A. (2024). Unintelligent adaptation: Animal spirits and

rainforest logics in the era of generative AI. In Academy of Management Proceedings (Annual

Meeting, Chicago, IL). Academy of Management.

Ifenthaler, D., & Yau, J. Y. K. (2020). Utilising learning analytics to support study success in

higher education: A systematic review. Educational Technology Research and Development, 68,

1961–1990. https://doi.org/10.1007/s11423-020-09795-5

Iranmanesh, A., & Lotfabadi, P. (2024). Critical questions on the emergence of text-to-image

artificial intelligence in architectural design pedagogy. AI and Society.

https://doi.org/10.1007/s00146-024- 02111-x

Jamal, A. (2023). The role of artificial intelligence (AI) in teacher education: Opportunities &

challenges. International Journal of Research and Analytical Reviews, 10(1), 139-146.

Jiménez-García, E., Martínez, N. O., & López-Fraile, L. A. (2024). Pedagogy Wheel for Artificial

Intelligence: adaptation of Carrington’s Wheel. RIED-Revista Iberoamericana de Educacion a

Distancia, 27(1), 87–113. https://doi.org/10.5944/ried.27.1.37622

Kapoor, J., Kaur, I., & Kaur, G. (2023). Artificial Intelligence Technology-Embedded

Learning: Rethinking Pedagogy for Digital Age. Proceedings of the 2nd International

Conference on Applied Artificial Intelligence and Computing, ICAAIC 2023, 87–93.

https://doi.org/10.1109/ICAAIC56838.2023.10140614

Keats, D., & Schmidt, J. P. (2007). The genesis and emergence of education 3.0 in higher

education and its potential for Africa. First Monday, 12(3).

Khan, A. I., Noor-ul-Qayyum, Shaik, M. S., Ali, A. M., &Bebi, C. V. (2012). Study of blended

learning process in education context. International Journal of Modern Education and

Computer Science, 4(9), 23-29

Kintu, M. J., Zhu, C., &Kagambe, E. (2017). Blended learning effectiveness: The relationship

between student characteristics, design features and outcomes. International Journal of

Educational Technology in Higher Education, 14(1), 1-20.

Kong, S. C., Lee, J. C. K., & Tsang, O. (2024). A pedagogical design for self-regulated learning in

academic writing using text-based generative artificial intelligence tools: 6-P pedagogy of

plan, prompt, preview, produce, peer-review, portfolio-tracking.Research and Practice

in Technology Enhanced Learning, 19. https://doi.org/10.58459/rptel.2024.19030

Kose, H., & Ozturk, M. (2022). The effectiveness of AI-powered personalized learning systems:

A meta-analysis. Computers & Education, 183, 104504.

https://doi.org/10.1016/j.compedu.2022.104504

Kroll, J. A., Huey, J., Barocas, S., Felten, E. W., Reidenberg, J. R., Robinson, D. G., & Yu, H.

(2016). Accountable algorithms. University of Pennsylvania Law Review, 165(3), 633–706.

https://scholarship.law.upenn.edu/penn_law_review/vol165/iss3/3

Kumari, K., & Murthy, C. R. K. (2024). A comparative study of blended learning and traditional learning:

Exploring academic achievement in secondary school. Scholarly Research Journal for Humanity

Science & English Language, 12(64), https://www.srjis.com/issues_data/235

Lai, C. L., & Tu, Y. F. (2024). Roles, strategies, and research issues of generative AI in the mobile

learning era. International Journal of Mobile Learning and Organisation, 18(4), 516-537.

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for

AI in education. Pearson Education.

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for

AI in education. Pearson.

Mandavkar, P. (2025). Indian Knowledge System (IKS) and National Education Policy (NEP-

2020). Available at SSRN 5205032.

Matthew, U. O., Kazaure, J. S., Kazaure, A. S., Onyedibe, O. N., & Okafor, A. N. (2022). The

Twenty First Century E-Learning Education Management & Implication for Media

Technology Adoption in the Period of Pandemic. EAI Endorsed Transactions on e-

Learning, 8(1).

McLoughlin, C., & Lee, M. J. W. (2008). The three P’s of pedagogy for the networked society:

Personalization, participation, and productivity. International Journal of Teaching and Learning

in Higher Education, 20(1), 10–27.

Min, W., & Yu, Z. (2023). A systematic review of critical success factors in blended learning.

Education Sciences, 13(5), 469. https://doi.org/10.3390/educsci13050469

Ministry of Education. (2020). National Education Policy 2020. Government of India.

https://www.education.gov.in

Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework

for teacher knowledge. Teachers College Record, 108(6), 1017–1054.

Molenaar, I., Knoop-van Campen, C., & Hassler, B. (2021). Teacher dashboards in AI-supported

classrooms: Opportunities and challenges for professional practice. Computers &

Education, 166, 104143. https://doi.org/10.1016/j.compedu.2021.104143

Moskal, P., Dziuban, C., & Hartman, J. (2013). Blended learning: A dangerous idea? The Internet

and Higher Education, 18, 15–23. https://doi.org/10.1016/j.iheduc.2012.12.001

Mourtzis, D., Panopoulos, N., & Angelopoulos, J. (2023). A hybrid teaching factory model

towards personalized education 4.0. International Journal of Computer Integrated

Manufacturing, 36(12), 1739-1759.

Nortvig, A., Petersen, A. K., & Balle, S. (2018). Blended learning. Journal of Turkish Science

Education, 8(2), 3–8.

Oliver, M., & Trigwell, K. (2005). Can “Blended Learning” be redeemed? E-learning, 2(1), 17-26

Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education:

Challenges and opportunities for sustainable development.

Rane, N. L., Chaudhari, R. A., & Rane, J. (2025). Critical Pedagogies and Artificial Intelligence: Teaching,

Curriculum, and Sustainable Education. Deep Science Publishing.

Redecker, C., Ala-Mutka, K., Bacigalupo, M., Ferrari, A., & Punie, Y. (2011). Learning 2.0: The

impact of Web 2.0 innovations on education and training in Europe. Publications Office of the

European Union.

Rudolph, J., Ismail, M. F. B. M., & Popenici, S. (2024). Higher education’s generative artificial

intelligence paradox: The meaning of chatbot mania. Journal of University Teaching and

Learning Practice, 21(6), 1–35. https://doi.org/10.53761/1.21.6.02

Rufino, L. G. C., Benevides, M. P., Benevides, K. D. G., dos Santos Goussain, B. G. C., & de

Moura, R. A. (2025). Gamification and Chatbots in Education: A Study on the Impact on

Student Interaction and Engagement. Journal of Information Systems Engineering and

Management, 10(27), 688-699.s44163-022-00039-z

Schildkamp, K. (2022). The digital turn in education: Opportunities and challenges for teacher

professional development. Teaching and Teacher Education, 111, 103625.

https://doi.org/10.1016/j.tate.2021.103625

Sharma, P. (2010). Blended learning. ELT Journal, 64(4), 456-458

Siddiqui, M. T., Mansoori, M. V., Siddiqui, M. A., & Yadav, A. (2025). AI-enabled pedagogy:

Advancing education through innovative teaching tools and the AI-TEACH model. Journal of

Informatics Education and Research, 5(1). https://doi.org/10.52783/jier.v5i1.2261

Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of

Instructional Technology and Distance Learning, 2(1), 3–10.

Siemens, G. (2005). Connectivism: A learning theory for the digital age. International Journal of

Instructional Technology and Distance Learning, 2(1), 3–10.

Singh, S. K., & Kumar, P. R. V. (2023). Need for Pedagogical Training of Teachers in Higher

Education Institutions: A Systematic Review. UNIVERSITY NEWS, 61, 37.

Sistemleri, B., Dergisi, Y. A., Makalesi, A., Research Article, /, & Savaş, S. (2021). Journal of

Information Systems and Management Research Artificial Intelligence and Innovative

Applications in Education: The Case of Turkey Yapay Zeka ve Eğitimde Yenilikçi

Uygulamalar: Türkiye Örneği MAKALE BİLGİSİ ÖZET.

http://dergipark.gov.tr/jismar

Southgate, E., Reynolds, R., & Howley, M. (2019). Artificial intelligence and education:

Perceptions of teachers in Australia. Australian Educational Researcher, 46(3), 341–361.

https://doi.org/10.1007/s13384-019-00325-w

Suárez, Á., Salmerón, L., & Rodríguez, E. (2023). The impact of AI lesson planners on teaching

innovation in bilingual education. Educational Technology & Society, 26(1), 54–68.

https://doi.org/10.30191/ETS.v26i1.1234

Tang, K. H. D. (2024). Implications of artificial intelligence for teaching and learning. Acta

Pedagogia Asiana, 3(2), 65-79.

Van Merriënboer , J. J. GKirschner, P. A. (2018). The Impact of Active Learning on Student

Achievement in Blended Learning Environments. Educational Psychology Review, 30(2),

105-126.

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard

University Press.

Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard

University Press.

W. Holmes (2021). Artificial intelligence in education (AIEd): A high-level academic and

industry overview, AI and Ethics, vol. 1, no. 2, pp. 123–129, 2021. [Online]. Available:

https://link.springer.com/article/10.1007/s43681-021-00074-z

Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI

in education. Learning, Media and Technology, 45(3), 223–235.

https://doi.org/10.1080/17439884.2020.1798995

Woolfitt, Z. (2015). The effective use of video in higher education. Lectoraat Teaching, Learning and

Technology Inholland University of Applied Sciences, 1(1), 1-49.

X. Chen et al., (2022)“The threat, hype, and promise of artificial intelligence in education: A

review,” AI and Ethics, vol. 2, no. 3, pp. 345–358, 2022. [Online]. Available:

ttps://link.springer.com/article/10.1007/

Xiang, H. (2022). A study on instructional design model. Journal of Shenzhen Polytechnic, 21(1), 52–

58.

Yao, J. (2024). The application of generative artificial intelligence in education: Potential, challenges, and

strategies. SHS Web of Conferences, 200, 02008.

https://doi.org/10.1051/shsconf/202420002008

Yao, X. (2024). Regulation challenges of online educational platforms in the age of artificial

intelligence. Journal of Educational Technology and Society, 27(1), 45–58. (update if exact source

differs)

Z. Slimi, (2023). The impact of artificial intelligence on higher education: An empirical study,

International Journal of Technology in Education, vol. 6, no. 2, pp. 123–135,. [Online].

Available: https://files.eric.ed.gov/fulltext/EJ1384682.pdf

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of

research on artificial intelligence applications in higher education – where are the

educators? International Journal of Educational Technology in Higher Education, 16, 1–27.

https://doi.org/10.1186/s41239-019-0171-0

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of

research on artificial intelligence applications in higher education. International Journal of

Educational Technology in Higher Education, 16(1), 1–27. https://doi.org/10.1186/s41239-

019-0171-0

Zhang, S., Yi, J., Li, Z., et al. (2022). Frontiers and trends of blended learning research in China

based on visualization analysis of CNKI database. International Journal of Information and

Communication Technology Education, 18(2), 1–13.

https://doi.org/10.4018/IJICTE.20220401.oa1

Zhou, X., Yu, Y., & Zeng, Z. (2021). Personalized learning: An AI-driven solution for learning

diversity. Interactive Learning Environments, 29(6), 827–839.

https://doi.org/10.1080/10494820.2019.1610456