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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue V, May 2026

Adaptability and Innovation of Artificial Intelligence in Educational

Contexts: A Conceptual Framework

Prof (Dr) Sajna Jaleel

, Mariya George

Professor & Head, School of Pedagogical Sciences, M.G. University

Mariya George, Research Scholar, School of Pedagogical Sciences, M.G. University

DOI: https://doi.org/10.51583/IJLTEMAS.2026.150500155

Received: 13 May 2026; Accepted: 18 May 2026; Published: 10 June 2026

ABSTRACT

Artificial Intelligence (AI) is transforming the education sector at a radical pace by providing personalized

instruction and by providing simulations to enhance the visualization skills of learners so that teaching-learning

process become a fruitful experience. AI in education encompasses Intelligent Tutoring Systems (ITS), AI-based

tests and administrative automation. India has started integrating AI into education policies through the efforts

of the All-India Council for Technical Education (AICTE) and the National Education Policy (National

Education Policy 2020 Ministry of Human Resource Development Government of India, 2020). This research

paper aims to analyze AI’s impact on education; innovative practices incorporated in educational scenarios as a

part of AI-Integration across various disciplines and to design a conceptual framework based on the adaptability

and innovation of Artificial Intelligence in educational contexts.

Keywords: Adaptability, Innovation, Artificial Intelligence, Educational Contexts, Conceptual Framework

INTRODUCTION

Information technologies, particularly artificial intelligence (AI), are revolutionizing modern education. AI

algorithms and educational robots are now integral to learning management and training systems, providing

support for a wide array of teaching and learning activities (Costa et al., 2017, García et al., 2007).Artificial

Intelligence (AI) has the potential to address some of the biggest challenges in education today, innovate teaching

and learning practices, and accelerate progress towards SDG 4 (Artificial Intelligence in Education, 2026). The

field of education especially lends itself to AI technologies since educational activities, including learning and

teaching, are knowledge-intensive cognitive activities, and AI applications, which are created for cognition and

problem-solving based on algorithms and knowledge base, can effectively support and augment educators’ and

learners’ abilities in teaching and learning. Since the advent of AI in the mid-1950 s, AI technologies have been

increasingly applied to facilitate education and training in various subjects, including language, STEM, and

medicine (Perrotta & Selwyn, 2020). To date, AIED applications are developed to support teaching and learning

activities such as content preparation and dissemination, interactions and collaboration, and performance

assessment (Chassignol et al., 2018, Perrotta and Selwyn, 2020). This paper explores the dual dimensions of

adaptability—the capacity of AI systems to adjust to individual learner needs—and innovation—the systemic

redesign of pedagogy facilitated by AI. We propose the "Integrated Socio-Technical Adaptability

Framework" (ISTAF), which categorizes AI integration into three layers: the Micro (Learner), Meso

(Institutional), and Macro (Societal).

Research Question

While AI technologies offer unprecedented "personalization," there is a significant gap between technical

capability and pedagogical effectiveness. Many AI implementations focus on "automated instruction"—simply

delivering content faster—rather than fostering "cognitive autonomy."). While AI offers efficiency, it risks

eroding critical thinking through excessive cognitive offloading, where tasks are transferred to AI, weakening

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individual cognitive skills. Without a conceptual framework, innovation remains fragmented, leading to issues 
of digital inequity and the erosion of critical thinking. 
Objectives 
  To analyze AI’s impact on education;  
  To examine innovative practices incorporated in educational scenarios as a part of AI-Integration across 
various disciplines. 
  To develop a conceptual framework (ISTAF) for the ethical and effective deployment of AI. 
  To evaluate the shift from AI-assisted teaching to AI-integrated pedagogy. 
LITERATURE REVIEW 
Impact of AI on Education 
AI in student learning 
AI-based environments have been used to personalize tasks for student learning. For example, Hirankerd and 
Kittisunthonphisarn  (2020) built  an AI-integrated  management  system  with  augmented,  virtual,  and  mixed 
reality  technologies  to  monitor  student  learning  progress  for  assigning  adaptive  tasks; Kong  et  al. 
(2021) developed a virtual patient for medical student training; Munawar et al. (2018) created and developed an 
intelligent virtual laboratory to cater for students' needs by assigning laboratory tasks at an appropriate level; 
and Yang and  Shulruf (2019) used an AI-enhanced skin to provide real-time  feedback and adaptive tasks  to 
medical students.  
Most of the studies implemented AI chatbots and interactive books that allowed students to have conversations 
with machines about their learning. AI techniques emulate the processes of human thought using structures that 
contain the knowledge and experience of human experts. AI chatbots and books built with these techniques have 
been  applied  to  language  learning  to  help  students  develop  their  communication  abilities  through  ongoing 
dialogue (Chew & Chua, 2020; Kim et al., 2021; Koc-Januchta et al., 2020; Palasundram et al., 2019; Vazquez-
Cano et al.,  2021). Another common  use of AI has been  to  give students  timely guidance and feedback by 
analyzing their work and learning process (Fu et al., 2020; Porter & Grippa, 2020). For example, Bonneton-
Botte  et  al.  (2020) used  an AI  application  for  notebooks  to  recognize  and  acquire  kindergarten  students' 
handwriting  and  then  analyze  its  spatiotemporal  characteristics  (i.e.,  the  shape,  order,  and  direction  of  the 
segments). The application gave feedback to the students at the end of each writing session. Vahabzadeh et al. 
(2018) used AI-enabled smart glasses to improve the attention of autistic students by monitoring their socially 
aware emotions and behavior. 
AI technologies have been implemented to capture student learning data and facilitate interactions for more 
adaptive  digital  environments.  For  instance, Samarakou  et  al.  (2015) developed  an  advanced  e-learning 
enviroment  for  engineering  students. Kickmeier-Rust  and  Holzinger  (2019) designed  and  developed  a 
combinatorial  optimization  algorithm  (the  MAXMIN  ant  system)  that  was  useful  and  effective  in  adaptive 
games. Westera et al. (2020) used techniques, such as facial emotion recognition, automatic difficulty adaptation, 
and stealth assessment, to profile students and applied techniques, such as non-verbal bodily motion and lip-
synchronized  speech,  to  develop  non-playing  characters.  The  student  profiles  and  characters  enhanced  the 
adaptability and interactivity of learning.  
 Intelligent tutoring systems aim to recommend teaching content and tasks that are appropriate for teaching needs 
(Aldeman et al., 2021; Bellod et al., 2021; McCarthy et al., 2016; Weragama & Reye, 2014). For example, Luo 
(2018) and Standen et al. (2020) adopted AI systems using multimodal sensor data to identify students' affective 
statuses and help teachers determine the optimal presentation of content, teaching methods, and communication 
strategies. Lampos  et  al.  (2021) used  an AI  classifier  to  recommend  effective  communication  strategies  for 
teachers to teach autistic students by analyzing student responses and attributes. In the study of Crowe et al. 

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(2017), teachers adjusted their teaching strategies based on the instant feedback provided by an academic writing 
software package on individual and whole class processing of learning material.  
The combination of computer assisted instruction and AI technologies has been  applied  to helping  teachers 
manage  their  classroom  teaching  (D. Yang,  Oh,  &  Wang,  2020; Jaiswal  &  Arun,  2021; Nabiyev  et  al., 
2013; Wang & Zheng, 2020; Zhang, 2021a, Zhang, 2021b). AI technologies have been used to support teaching 
in different subject classrooms (e.g., physical and language education) by efficiently uploading, assigning, and 
distributing learning materials and assignments and by speaking out text-based problems. These applications 
have greatly improved the efficiency of classroom management for teachers (Gupta & Bhaskar, 2020; Huang et 
al., 2021; Jarke & Macgilchrist, 2021; Rapanta & Walton, 2016). 
AI technologies have been applied not only to support teaching but also to support the professional development 
of teachers (Gunawan et al., 2021; Lampos et al., 2021). In these studies, teachers were given suggestions and 
comments on  their teaching by AI  agents  that  analyzed  real-time  data  in  classrooms, such as behavior and 
questioning skills, and teachers' responses to diagnostic tests of their pedagogical content knowledge. Teaching 
evaluation models have also been built from teaching data (Hu, 2021; Li & Su, 2020). 
Analysis showed that the use of AI to enhance and automate assessment resulted in more effective grading (Aebi 
& Karal, 2017; Alghamdi et al.,  2020; Fu et  al., 2020; Kumar &  Boulanger, 2020; Ma & Slater;  2015). AI-
enhanced grading systems for language writing and speaking and mathematics provided more accurate, fast, and 
secure grading in tests and examinations than teachers. The systems were also able to return immediate marks 
for formative feedback in online learning.  
AI  technologies  appear  to  have  assisted  in  predicting  student  performance, particularly in  online  education 
(Akmese et al., 2021; Costa-Mendes et al., 2021; Yu, 2021). They have shown a capacity to predict students' 
performance in online courses by assessing the extent and quality of their participation in learning activities, 
such as discussion forums. This functionality is very important for distance education and MOOCs due to the 
absence of teachers. However, selecting data for prediction is challenging. Costa-Mendes et al. (2021) argued 
that the student data used for classic statistics may not fit AI predictive models. 
CONCEPTUAL  FRAMEWORK  (ISTAF)  FOR  THE  ETHICAL  AND  EFFECTIVE 
DEPLOYMENT OF AI. 
With  a  strong theoretical  base  on  Socio-Technical  Systems Theory,  Integrated Socio-Technical Adaptability 
Framework" (ISTAF) categorizes AI integration into three layers: the Micro (Learner), Meso (Institutional), and 
Macro (Societal). Within a socio-technical systems perspective, any organisation, or part of it, is made up of a 
set of interacting sub-systems, as shown in the diagram below. 
 
Socio-technical  theory  has  at  its  core  the  idea  that  the  design  and  performance  of  any  organisational 
system can only be understood and improved if both ‘social’ and ‘technical’ aspects are brought together 

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and treated as interdependent parts of a complex system. Socio-Technical Systems Theory (STST) offers

such a perspective by conceptualizing organizations as dynamic configurations of interacting technical and social

subsystems whose ongoing negotiation and adaptation fundamentally shape the outcomes of technological

innovation (Bostrom & Heinen, 1977; Mumford, 2006; Pasmore et al., 2019; Trist & Bamforth, 1951). Rather

than treating technology and organization as isolated variables, STST views innovation as an emergent,

processual phenomenon in which changes to AI systems are intertwined with evolving work routines,

governance mechanisms, data infrastructures, and decision-making practices (Akter et al., 2022; Cannas, 2023;

Chaudhuri et al., 2024; Pfaff et al., 2023).

Integrated Socio-Technical Adaptability Framework (ISTAF) categorizes AI integration into three layers: the

Micro (Learner), Meso (Institutional), and Macro (Societal). The Integrated Socio-Technical Adaptability

Framework (ISTAF) conceptualizes AI as a multi-layered socio-technical ecosystem in which successful

integration depends on the adaptive alignment of human, technical, institutional, and systemic elements across

micro, meso, and macro layers.

Layer 1: Micro AI Layer (User level)

This layer points to the use of various AI tools, personalized tutoring AI, AI writing support, AI feedback

systems, AI productivity tools, AI for problem-solving by students, teachers, faculty, administrators and learners

across various disciplines. Adaptability Constructs at this layer encompasses cognitive adaptability, digital

adaptability, AI self-efficacy and ethical adaptability.

Layer 2: Meso AI Layer (Institutional/Organizational Level)

This layer emphasizes upon AI as embedded in institutional structures and practices. The core focus at this layer

includes LMS-Integrated AI, AI-enabled assessment systems, institutional chatbots, AI-supported teaching

design and AI analytics for student monitoring. Socio-technical focus at this layer happens in the form of

interaction between institutional culture, infrastructure, governance, and pedagogical integration. Adaptability

Constructs at this layer consists of institutional adaptability, curriculum adaptability and organizational

readiness.

Layer 3: Macro AI Layer (Provider/ Policy/Ecosystem Layer)

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AI as part of a broader ecosystem shaped by providers, regulation and policy. AI at this layer act as a large AI

platform and policy-guided AI ecosystems. At this layer, socio-technical focus specially points to the interaction

between regulation, accessibility, affordability, sustainability and equity. Adaptability Constructs at this layer

includes systematic adaptability, policy adaptability and ecosystem resilience.

RESULTS/DISCUSSION

Even though AI has far and wide- reaching applications as mentioned in the Review of Related Literature across

student learning, teaching and assessment, there are certain points to be addressed as selecting appropriate data

for student performance predictive models remains challenging as the data are not the same as those used in

traditional educational research, without a grasp of the mechanism of task assignment and teaching strategy

recommendations, teachers have reported feeling that their control was diminished and that they were working

with a black box. The resulting decline in self-efficacy may discourage teachers from using AI to support their

classroom teaching. The study throws light into a conceptual framework (ISTAF) through which AI -integration

across disciplines become sustainable and educationally meaningful when AI is understood as a three-layer

socio-technical ecosystem in which adaptability operates across individual, institutional, and provider-policy

levels.

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