INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,  
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)  
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XV, Issue VI, June 2026  
A Multidimensional Conceptual Framework of Generative AI as a  
Co-Instructor: Measurement Formulation for Personalized and  
Sustainable Higher Education  
Jonathan M. Mantikayan1, Montadzah A. Abdulgani2  
1Bangsamoro Information and Communications Technology Office (BICTO-BARMM)  
2Cotabato State University  
DOI: https://doi.org/10.51583/IJLTEMAS.2026.150600148  
Received: 29 June 2025; Accepted: 06 July 2026; Published: 20 July 2026  
ABSTRACT  
Generative artificial intelligence (GenAI) is transforming higher education by evolving from a content  
automation tool into a collaborative instructional partner. However, its conceptualization as a co-instructor  
remains fragmented and lacks a validated measurement structure. This study develops a multidimensional  
conceptual framework and measurement formulation that positions GenAI as a structured pedagogical  
collaborator supporting personalization, sustainability, and ethical integrity. Using a theory-building approach  
grounded in interdisciplinary literature, the study defines key constructs, specifies relationships, and proposes  
indicators for future structural equation modeling. The framework identifies three instructional domains co-  
planning, co-instruction, and co-assessment mediated by pedagogical implementation effectiveness and  
moderated by ethical governance readiness. An eight-construct model and 37-item instrument are proposed for  
empirical validation. The study contributes conceptual clarity by advancing a systematic, ethically grounded  
approach to human–AI collaboration in higher education and recommends institutionally guided, teacher-in-the-  
loop implementation strategies.  
Keywords: generative AI, co-instructor model, measurement development, higher education, ethical AI  
governance  
INTRODUCTION  
Generative artificial intelligence (GenAI) technologies are rapidly transforming the landscape of higher  
education by introducing new possibilities for personalized learning, instructional scalability, and pedagogical  
innovation. Powered by large language models and advanced machine learning architectures, AI-driven systems  
can now assist with a wide range of academic functions, including curriculum design, instructional content  
generation, adaptive tutoring, automated assessment, and feedback provision (Dwivedi et al., 2023; Kasneci et  
al., 2023). These capabilities have prompted universities worldwide to explore the integration of GenAI tools  
into teaching and learning environments as a means to improve instructional efficiency and enhance student  
engagement. As higher education institutions strive to develop more personalized learning pathways and resilient  
academic ecosystems, GenAI is increasingly recognized as a transformative technological enabler.  
Despite its growing adoption, scholarly discourse on GenAI in higher education remains conceptually  
fragmented. Existing research largely frames AI either as a productivity-enhancing tool that automates routine  
academic tasks or as a disruptive force that may undermine academic integrity, critical thinking, and authentic  
learning processes (Kasneci et al., 2023; Tlili et al., 2023). While these perspectives contribute valuable insights,  
they often overlook the potential for structured human–AI collaboration within instructional processes.  
Emerging pedagogical frameworks, such as the Teacher-in-the-Loop model, propose that when implemented  
within clearly defined instructional and governance structures, GenAI can function not merely as a supportive  
tool but as a collaborative partner that complements human expertise in teaching and learning activities  
(Shubhanshi, 2025). This evolving perspective suggests a shift from a tool-centric view of AI toward a  
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partnership-based paradigm in which human instructors and AI systems jointly contribute to instructional design,  
delivery, and evaluation.  
The conceptualization of GenAI as a co-instructor raises important theoretical and methodological questions.  
Currently, there is limited consensus on how to define, structure, and empirically measure the role of GenAI as  
a pedagogical collaborator. Without a coherent conceptual framework and validated measurement model,  
research on AI-assisted teaching risks remaining fragmented, descriptive, and context-specific rather than  
cumulative and theory-driven. A comprehensive framework is therefore needed to capture the multidimensional  
nature of AI-supported instruction one that integrates instructional collaboration processes, pedagogical  
effectiveness, ethical governance considerations, and measurable educational outcomes.  
Addressing this need requires grounding the emerging role of GenAI in established theoretical perspectives.  
Prior research in educational technology highlights the relevance of models such as the Technology Acceptance  
Model, which explains users’ adoption of new technologies based on perceived usefulness and ease of use  
(Davis, 1989); the Information Systems Success Model, which evaluates system effectiveness through  
dimensions such as system quality, information quality, and user satisfaction (DeLone and McLean, 2003); and  
Student Engagement Theory, which emphasizes the role of active participation in improving learning outcomes  
(Fredricks et al., 2004). In addition, pedagogical frameworks such as Formative Assessment Theory underscore  
the importance of continuous feedback and assessment in enhancing instructional effectiveness (Black &  
Wiliam, 1998), while emerging discussions on Responsible AI Governance emphasize ethical transparency,  
accountability, and human oversight in AI-enabled systems (Floridi et al., 2018).  
Building upon these theoretical perspectives, this study addresses the conceptual gap by developing a  
multidimensional framework that positions GenAI as a collaborative instructional partner in higher education.  
Specifically, this paper aims to: (1) develop a multidimensional conceptual framework that positions Generative  
AI as a co-instructor in higher education, and (2) formulate a theory-grounded measurement structure that  
enables future empirical validation of this framework.  
It is important to emphasize that the present study is intentionally conceptual in nature. Rather than empirically  
testing the proposed framework, the study focuses on developing a theoretically grounded conceptual model and  
a preliminary measurement structure that can guide future quantitative investigations. Accordingly, empirical  
validation of the proposed 37-item instrument including assessments of reliability, construct validity, convergent  
validity, discriminant validity, and factor structure through confirmatory factor analysis (CFA) and structural  
equation modeling (SEM) is proposed as the next phase of this research program. This staged approach is  
consistent with established theory-building research, in which conceptual model development and construct  
specification precede empirical validation and theory testing (Jaakkola, 2020; MacInnis, 2011; Whetten, 1989).  
Theoretical Foundations  
The conceptualization of Generative Artificial Intelligence (GenAI) as a co-instructor in higher education  
requires grounding in established theoretical perspectives from information systems, educational psychology,  
and technology governance. This study integrates five complementary theoretical lenses - the Technology  
Acceptance Model (TAM), the Information Systems Success Model, Student Engagement Theory, Formative  
Assessment Theory, and Responsible AI Governance frameworks - to explain how GenAI-supported  
instructional collaboration may influence pedagogical effectiveness and educational outcomes. Together, these  
theories provide a multidimensional foundation for understanding the interaction between technology adoption,  
instructional practices, ethical oversight, and learning performance.  
Technology Acceptance Model  
The Technology Acceptance Model (TAM), originally proposed by Davis (1989), explains how users come to  
accept and utilize new technologies. TAM posits that perceived usefulness and perceived ease of use determine  
an individual’s intention to adopt technological systems (Davis, 1989). In the context of higher education,  
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instructors’ willingness to integrate GenAI tools into teaching practices depends largely on their perception that  
these systems enhance instructional efficiency and improve learning outcomes.  
Within this study, TAM provides the theoretical basis for understanding instructors’ engagement with GenAI in  
collaborative instructional processes such as co-planning, co-instruction, and co-assessment. When educators  
perceive AI-assisted tools as useful for generating teaching materials, facilitating instructional delivery, or  
supporting assessment activities, they are more likely to incorporate these technologies into their pedagogical  
workflows. Consequently, higher levels of technology acceptance are expected to facilitate more effective  
human–AI instructional collaboration, thereby contributing to improved teaching performance and learning  
outcomes.  
Information Systems Success Model  
The Information Systems Success Model developed by William H. DeLone and Ephraim R. McLean provides a  
widely used framework for evaluating the effectiveness of information systems. The model identifies several  
dimensions of system success, including system quality, information quality, service quality, user satisfaction,  
and net benefits (DeLone and McLean, 2003).  
Applied to AI-supported educational environments, this model suggests that the effectiveness of GenAI as a co-  
instructor depends on the reliability, accuracy, and usability of the AI system. High-quality AI-generated  
instructional materials, responsive feedback mechanisms, and reliable system performance can enhance both  
instructor satisfaction and student learning experiences. In this study, the Information Systems Success Model  
informs the evaluation of Pedagogical Implementation Effectiveness (PIE) and helps explain how the quality of  
AI-assisted instructional processes contributes to Instructor Outcomes (IO) and Student Outcomes (SO).  
Student Engagement Theory  
Student Engagement Theory emphasizes the importance of active participation in learning activities as a critical  
determinant of academic success. Scholars such as Jennifer A. Fredricks conceptualize engagement as a  
multidimensional construct encompassing behavioral, emotional, and cognitive components (Fredricks et al.,  
2004). Higher levels of engagement are associated with improved motivation, deeper learning, and stronger  
academic performance.  
In AI-supported learning environments, GenAI tools can facilitate interactive learning experiences by generating  
adaptive learning materials, personalized explanations, and timely feedback. These capabilities can increase  
students’ cognitive engagement with course content and encourage active participation in the learning process.  
Within the proposed framework, enhanced engagement mediated by AI-assisted instructional practices is  
expected to contribute directly to improved Student Outcomes, including deeper understanding, higher  
satisfaction, and stronger academic achievement.  
Formative Assessment Theory  
Formative Assessment Theory highlights the role of continuous assessment and feedback in improving teaching  
and learning processes. According to the seminal work of Paul Black and Dylan Wiliam, formative assessment  
enables instructors to monitor student progress, identify learning gaps, and adjust instructional strategies  
accordingly (Black and Wiliam, 1998).  
GenerativeAI systems can enhance formative assessment practices by providing automated feedback, generating  
assessment questions, and assisting instructors in evaluating student work. These capabilities enable a more  
responsive and adaptive instructional environment in which assessment becomes an ongoing component of the  
learning process. In this study, Formative Assessment Theory supports the construct of Co-Assessment (CA),  
where instructors and AI systems collaboratively analyze student performance and provide feedback that  
improves both teaching effectiveness and student learning outcomes.  
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Responsible AI Governance  
The increasing integration of AI technologies in educational settings raises important ethical and governance  
considerations. Responsible AI Governance frameworks emphasize principles such as transparency,  
accountability, fairness, and human oversight in the design and deployment of AI systems (Floridi et al., 2018).  
These principles are essential to ensuring that AI technologies are implemented in ways that support educational  
integrity and protect the rights of learners and educators.  
Within the proposed conceptual framework, responsible governance mechanisms are captured through the  
construct of Ethical Governance Readiness (EGR). This construct reflects the extent to which institutions  
establish policies, ethical guidelines, and oversight mechanisms for the responsible use of AI in teaching and  
learning. Strong governance frameworks help mitigate risks related to bias, misinformation, and overreliance on  
automated systems while ensuring that human educators retain ultimate pedagogical authority.  
The Proposed Conceptual Framework  
The proposed conceptual framework conceptualizes Generative Artificial Intelligence (GenAI) as a  
multidimensional instructional collaborator operating across interconnected pedagogical domains within higher  
education. Rather than viewing AI solely as a technological tool, the framework positions GenAI as a  
complementary instructional partner that supports instructors throughout the teaching and learning process. This  
perspective reflects an emerging paradigm of human–AI collaborative pedagogy, in which artificial intelligence  
augments human expertise while instructors retain pedagogical authority and oversight.  
At the core of the framework are three primary instructional collaboration domains. The first domain, co-  
planning, refers to the extent to which GenAI assists instructors in instructional design and preparation activities.  
This includes supporting curriculum development, generating teaching materials, organizing lesson structures,  
and providing suggestions for instructional strategies. By facilitating these preparatory tasks, GenAI can help  
instructors streamline course planning processes and devote greater attention to higher-order pedagogical  
decisions (Zhang, 2025).  
The second domain, co-instruction, represents the role of GenAI in supporting the delivery of instruction.  
Through capabilities such as personalized explanations, adaptive tutoring, interactive question–answering, and  
on-demand learning assistance, GenAI can complement traditional instructional methods and support  
individualized learning pathways. These capabilities enable instructors to extend instructional support beyond  
the physical classroom, thereby enhancing learning accessibility and responsiveness to diverse student needs  
(Neupane et al., 2024).  
The third domain, co-assessment, captures the collaborative role of GenAI in student evaluation and feedback  
processes. GenAI systems can assist instructors in generating assessment questions, supporting grading  
processes, developing evaluation rubrics, and providing timely formative feedback. These functions allow  
instructors to monitor student progress more effectively and facilitate continuous improvement in learning  
outcomes through data-informed instructional adjustments (Xiao et al., 2024).  
Beyond these core instructional domains, the framework incorporates Pedagogical Implementation  
Effectiveness, which represents the extent to which GenAI is integrated into teaching practices in a structured,  
purposeful, and pedagogically meaningful manner. Drawing on the Teacher-in-the-Loop approach, this construct  
emphasizes that while AI systems may support instructional processes, ultimate pedagogical authority and  
professional judgment remain with human instructors. This human-centered oversight ensures that AI-supported  
teaching practices remain aligned with educational objectives, disciplinary standards, and ethical considerations  
(Shubhanshi, 2025).  
In addition, the framework introduces Ethical Governance Readiness as an important contextual factor  
influencing the responsible adoption of AI in education. Ethical governance readiness refers to the degree to  
which institutions establish policies, guidelines, and oversight mechanisms that ensure responsible AI  
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deployment. These mechanisms include safeguards for mitigating algorithmic bias, protecting student data  
privacy, preserving academic integrity, and promoting transparency in AI-assisted decision-making processes  
(Franco et al., 2024). Strong governance structures help ensure that AI technologies are implemented in ways  
that support educational values while minimizing potential risks.  
The combined influence of these constructs is expected to shape multiple categories of outcomes. Instructor  
outcomes include improvements in teaching efficiency, instructional innovation, and professional satisfaction  
resulting from AI-supported teaching practices. Student outcomes encompass enhanced engagement, improved  
academic performance, and deeper critical thinking through personalized and adaptive learning experiences. At  
the institutional level, institutional outcomes reflect broader organizational benefits such as digital  
transformation, scalable personalized education, and strengthened capacity for technology-enabled learning.  
Collectively, the proposed framework provides a structured model for examining how GenAI can function as a  
sustainable, collaborative, and ethically governed co-instructor within higher education environments. By  
integrating instructional collaboration processes, implementation conditions, governance considerations, and  
educational outcomes, the model offers a comprehensive foundation for future empirical investigation of human–  
AI instructional partnerships.  
Figure 1 Conceptual framework of Generative AI as a Co-Instructor in Higher Education.  
Conceptual Novelty of the GenAI Co-Instructor Model  
The rapid advancement of Generative Artificial Intelligence (GenAI) has stimulated a growing body of research  
examining its applications in higher education. Existing studies predominantly conceptualize AI as an  
instructional support technology that functions as an intelligent tutor, virtual assistant, conversational chatbot, or  
adaptive learning system (Holmes et al., 2022; Kasneci et al., 2023; UNESCO, 2023; Zawacki-Richter et al.,  
2019). Within these perspectives, AI is primarily viewed as a tool that automates academic tasks, delivers  
personalized learning resources, answers students' questions, or facilitates individualized instruction (Dwivedi  
et al., 2023; Tlili et al., 2023). Although these approaches have demonstrated considerable value in improving  
learning efficiency, accessibility, and instructional scalability, they generally position AI as an auxiliary  
technology operating independently from the pedagogical responsibilities of human educators (Holmes et al.,  
2022).  
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The present study advances this discourse by proposing a fundamentally different conceptualization of GenAI.  
Rather than treating AI solely as an instructional tool or automated learning assistant, this framework positions  
GenAI as a collaborative co-instructor that actively participates throughout the instructional process while  
remaining under the pedagogical authority of human educators. In this model, AI complements rather than  
replaces the instructor by supporting three interconnected instructional domains: co-planning, co-instruction,  
and co-assessment. These domains collectively represent the complete instructional lifecycle, extending AI  
involvement beyond isolated teaching tasks toward sustained human–AI instructional collaboration  
(Shubhanshi, 2025; Zhang, 2025).  
Unlike conventional AI-assisted learning models that primarily emphasize student interaction with intelligent  
systems, the proposed framework adopts a Teacher-in-the-Loop (TiTL) perspective in which instructors retain  
instructional authority, professional judgment, and ethical responsibility. GenAI serves as an instructional  
collaborator that augments curriculum development, facilitates adaptive learning experiences, assists in  
formative assessment, and enhances instructional effectiveness without diminishing the central role of educators.  
This human-centered perspective aligns with emerging Responsible AI principles advocating meaningful human  
oversight, transparency, accountability, fairness, and pedagogical integrity in educational AI implementation  
(Floridi et al., 2018; OECD, 2019; UNESCO, 2023).  
Furthermore, the proposed framework extends beyond existing conceptual models by integrating instructional  
collaboration processes with contextual implementation and governance mechanisms. Specifically, Pedagogical  
Implementation Effectiveness (PIE) is introduced as a mediating construct that explains how structured  
instructional integration translates AI-supported practices into meaningful educational outcomes.  
Simultaneously, Ethical Governance Readiness (EGR) is incorporated as a moderating construct reflecting  
institutional preparedness to ensure responsible, transparent, and ethical AI deployment. These additions  
recognize that successful AI integration depends not only on technological capability but also on pedagogically  
sound implementation strategies and robust institutional governance structures (Dwivedi et al., 2023; Franco et  
al., 2024).  
Collectively, this multidimensional perspective differentiates the proposed framework from previous AI-in-  
education models by conceptualizing GenAI as a collaborative instructional partner operating across planning,  
teaching, and assessment within ethically governed and human-supervised educational environments.  
Consequently, the framework provides a more comprehensive theoretical foundation for examining human–AI  
instructional collaboration and establishes a structured basis for future empirical validation through measurement  
development and structural equation modeling.  
The proposed framework also represents the continuation of the authors' long-term research agenda on digital  
transformation and technology-enabled learning. Previous studies examined the role of interactive educational  
technologies in enhancing classroom learning experiences (Mantikayan and Ayu, 2010) and organizational  
readiness for digital government transformation (Mantikayan and Abdulgani, 2017). Building upon these earlier  
contributions, the present study advances a new conceptual perspective by positioning Generative AI as a  
collaborative co-instructor supported by pedagogical implementation effectiveness and ethical governance  
readiness.  
Hypothesis Development  
Generative AI as a Co-Instructor in Higher Education  
The integration of Generative Artificial Intelligence (GenAI) in higher education is increasingly conceptualized  
not merely as a technological tool, but as a pedagogical collaborator capable of augmenting instructional design,  
delivery, and assessment processes (Dwivedi et al., 2023; UNESCO, 2023). Framed as a co-instructor, GenAI  
supports educators through co-planning, co-instruction, and co-assessment functions. Emerging research  
suggests that AI systems can function as cognitive partners, enhancing teaching efficiency while preserving  
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human pedagogical oversight (Holmes et al., 2022). This study advances a structural model examining how these  
functional roles influence instructor, student, and institutional outcomes, while accounting for implementation  
frameworks and ethical governance safeguards.  
Direct Effects of GenAI Functional Roles  
GenAI Co-Planning and Instructor Outcomes  
GenAI-assisted co-planning enables instructors to automate lesson design, generate instructional materials, and  
streamline administrative preparation. Research on AI-supported instructional design indicates improvements in  
efficiency, instructional alignment, and creative ideation when AI is used collaboratively (Holmes et al., 2022;  
Luckin et al., 2016). By reducing routine cognitive workload, educators can allocate greater effort toward  
pedagogical innovation and student engagement, consistent with cognitive load theory (Sweller, 1988).  
H1a: GenAI-supported co-planning positively influences instructor outcomes.  
GenAI Co-Instruction and Student Outcomes  
GenAI-enabled co-instruction provides adaptive tutoring, real-time clarification, and personalized feedback.  
Adaptive learning and intelligent tutoring research demonstrate that personalized scaffolding improves  
comprehension, engagement, and performance (VanLehn, 2011; Zawacki-Richter et al., 2019). When AI systems  
supplement classroom instruction, students benefit from differentiated explanations and continuous assistance.  
H1b: GenAI-supported co-instruction positively influences student outcomes.  
GenAI Co-Assessment and Student Outcomes  
AI-driven co-assessment systems facilitate automated scoring, rubric generation, and formative feedback  
delivery. Timely and consistent feedback significantly enhances learning gains and conceptual understanding  
(Hattie, 2009; Shute, 2008). Automated feedback systems also improve scalability and responsiveness in large  
classes (Zawacki-Richter et al., 2019).  
H1c: GenAI-supported co-assessment positively influences student outcomes.  
Mediating Role of Implementation Frameworks  
The effectiveness of GenAI integration depends on structured implementation models, including teacher-in-the-  
loop validation, hybrid human - AI collaboration, and retrieval-augmented generation (RAG). Human-centered  
AI frameworks emphasize complementarity between human judgment and machine efficiency (Raisch and  
Krakowski, 2021). Without structured implementation, AI systems risk producing biased or inaccurate outputs  
(UNESCO, 2023). Thus, implementation framework effectiveness is proposed as a mediating mechanism  
through which GenAI functional roles translate into improved outcomes.  
H2a: Implementation framework effectiveness mediates the relationship between GenAI co-planning and  
instructor outcomes.  
H2b: Implementation framework effectiveness mediates the relationship between GenAI co-instruction and  
student outcomes.  
H2c: Implementation framework effectiveness mediates the relationship between GenAI co-assessment and  
student outcomes.  
Moderating Role of Ethical and Governance Safeguards  
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The integration of GenAI raises concerns regarding academic integrity, algorithmic bias, data privacy, and  
teacher autonomy (Dwivedi et al., 2023; UNESCO, 2023). Responsible AI governance frameworks stress  
transparency, accountability, and fairness (OECD, 2019). Strong governance safeguards are expected to  
strengthen positive relationships between GenAI functions and outcomes.  
H3a: Ethical and governance safeguards positively moderate the relationship between GenAI co-planning and  
instructor outcomes.  
H3b: Ethical and governance safeguards positively moderate the relationship between GenAI co-instruction and  
student outcomes.  
H3c: Ethical and governance safeguards positively moderate the relationship between GenAI co-assessment and  
student outcomes.  
Institutional-Level Outcomes  
Structured and ethically governed GenAI integration contributes to scalable and sustainable digital  
transformation in higher education institutions (Raisch and Krakowski, 2021; UNESCO, 2023). Institutions  
embedding hybrid AI models within robust governance systems are better positioned to achieve long-term  
innovation and competitive advantage.  
H4: Effective implementation frameworks positively influence institutional outcomes.  
Table 1: Conceptual Distinction Between Existing AI-in-Education Models and the Proposed GenAI Co-  
Instructor Framework  
Existing  
AI Primary Focus  
Role of AI  
Human  
Limitation  
Proposed  
GenAI  
Conceptualization  
Instructor's  
Role  
Co-Instructor  
Framework  
Intelligent Tutor  
Student learning Personalized  
Supervises  
instruction  
Focuses mainly on AI  
collaborates  
instructors  
support  
tutoring  
student–AI  
interaction  
with  
throughout  
the  
instructional  
process  
Virtual Assistant  
Administrative  
and  
Generates  
materials and decision-maker  
Primary  
Supports isolated AI  
teaching tasks participates  
actively  
in  
instructional  
assistance  
answers  
queries  
planning,  
instruction,  
assessment  
and  
Conversational  
Chatbot  
Question  
answering  
Information  
provider  
Oversees  
use  
AI Limited  
AI functions as a  
pedagogical  
collaborator within  
structured teaching  
practices  
pedagogical  
integration  
Adaptive Learning Personalized  
Adapts content Configures  
Emphasizes  
Integrates  
System  
learning  
to learners  
learning  
learner adaptation instructional  
pathways  
environment  
rather  
instructional  
collaboration  
than collaboration,  
implementation  
effectiveness, and  
governance  
readiness  
Proposed  
Co-Instructor Model collaborative  
pedagogy  
GenAI Human–AI  
Collaborative  
instructional  
partner  
Retains  
Addresses  
instructional,  
pedagogical, and instruction, and co-  
the governance assessment under  
Supports  
planning,  
co-  
co-  
pedagogical  
authority  
through  
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Teacher-in-the-  
Loop approach  
dimensions  
simultaneously  
ethical  
oversight  
human  
RESEARCH METHODOLOGY  
Research Design  
This study adopts a qualitative conceptual research design based on document analysis to develop a  
multidimensional framework that conceptualizes Generative Artificial Intelligence (GenAI) as a co-instructor in  
higher education. Document analysis is an established qualitative research method that involves the systematic  
examination and interpretation of existing scholarly materials in order to extract meaning, identify patterns, and  
develop theoretical insights (Bowen, 2009).  
The purpose of employing document analysis in this study is to synthesize insights from diverse streams of  
literature related to artificial intelligence in education, educational technology, instructional design, assessment  
practices, and responsible AI governance. By systematically reviewing and interpreting these documents, the  
study identifies key instructional functions and contextual factors that shape the integration of GenAI within  
higher education teaching and learning environments.  
The research process involved a structured review of peer-reviewed journal articles, conference papers, policy  
reports, and theoretical studies addressing AI-supported learning and digital pedagogy. The collected documents  
were analyzed to identify recurring themes, conceptual constructs, and relationships relevant to human-AI  
instructional collaboration. These insights informed the development of the proposed conceptual framework and  
its associated measurement constructs.  
Instrument Development  
Although the present study is conceptual in nature, it also proposes a measurement instrument that can support  
future empirical validation of the framework. The development of this instrument was guided by insights derived  
from the document analysis process.  
First, relevant constructs related to AI-supported instruction were identified through a systematic review of  
existing literature on educational technology adoption, AI-enabled learning systems, instructional design,  
formative assessment, and ethical AI governance. Key constructs frequently discussed in prior studies were  
extracted and categorized according to their pedagogical roles within higher education environments.  
Second, the identified constructs were synthesized and organized into a multidimensional structure that reflects  
the collaborative roles of GenAI across instructional planning, teaching, and assessment processes. Based on  
these constructs, measurement indicators were drafted to represent the conceptual dimensions of the framework.  
Third, the proposed measurement items were formulated in a way that allows future researchers to operationalize  
the constructs within survey-based or quantitative research designs, such as structural equation modeling. In this  
way, the present study contributes not only a conceptual framework but also a preliminary measurement structure  
that can facilitate future empirical research on human–AI instructional collaboration.  
Measurement Scale  
To enable future empirical testing of the framework, the proposed measurement items are designed to be  
measured using a five-point Likert scale, a commonly used approach in educational and information systems  
research. The Likert scale allows respondents to express their level of agreement with statements related to AI-  
supported instructional practices.  
The scale ranges from:  
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1 – Strongly Disagree  
2 – Disagree  
3 – Neutral  
4 – Agree  
5 – Strongly Agree  
Although the present study does not conduct empirical testing, the proposed measurement structure is designed  
to be suitable for future statistical validation using analytical techniques such as structural equation modeling  
(SEM). Future studies may evaluate the reliability and validity of the measurement model through procedures  
such as confirmatory factor analysis, reliability testing, and validity assessment.  
Constructs and Measurement  
Based on the document analysis, eight key constructs were identified as central to conceptualizing GenAI as a  
collaborative instructional partner in higher education. These constructs capture the functional roles of GenAI,  
the conditions supporting its implementation, and the outcomes associated with AI-supported teaching and  
learning.  
Co-Planning (CP)  
Co-planning refers to the extent to which GenAI supports instructors in instructional design and preparation  
activities. Document analysis of literature on AI-supported curriculum design and instructional planning  
indicates that AI tools can assist educators in generating teaching materials, organizing lesson structures, and  
developing course content.  
Co-Instruction (CI)  
Co-instruction represents the role of GenAI in supporting instructional delivery. Literature on intelligent tutoring  
systems and AI-powered learning assistants highlights the potential of AI technologies to provide personalized  
explanations, adaptive learning pathways, and interactive academic assistance that complement instructors’  
teaching efforts.  
Co-Assessment (CA)  
Co-assessment captures the role of GenAI in supporting student evaluation and feedback processes. Research on  
AI-enabled assessment systems demonstrates that AI tools can assist instructors in generating assessment  
questions, supporting grading processes, and providing timely formative feedback to students.  
Pedagogical Implementation Effectiveness (PIE)  
Pedagogical implementation effectiveness refers to the extent to which GenAI is integrated into teaching  
practices in a structured and pedagogically meaningful manner. Studies on human–AI collaboration in education  
emphasize the importance of maintaining instructor oversight to ensure that AI tools support rather than replace  
pedagogical decision-making.  
Ethical Governance Readiness (EGR)  
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Ethical governance readiness represents the institutional preparedness to ensure responsible AI deployment in  
educational settings. Document analysis highlights the importance of governance mechanisms that address issues  
such as algorithmic bias, academic integrity, data privacy, and transparency in AI-assisted decision-making.  
Instructor Outcomes (IO)  
Instructor outcomes refer to improvements in teaching efficiency, instructional innovation, and professional  
productivity that may result from the integration of GenAI into instructional practices.  
Student Outcomes (SO)  
Student outcomes capture improvements in learning experiences, including higher levels of engagement,  
improved academic performance, and enhanced critical thinking resulting from personalized and adaptive AI-  
supported learning environments.  
Institutional Outcomes (INO)  
Institutional outcomes represent broader organizational benefits associated with AI adoption in higher education,  
including digital transformation, scalable personalized education, and improved institutional competitiveness.  
Measurement Foundations  
The following table outlines the key variables underpinning the study, along with the theoretical foundations and  
primary sources that inform each construct. These variables ranging from co-planning and co-instruction to  
ethical governance readiness and outcome constructs are grounded in established frameworks, pedagogical  
theories, and emerging research on AI integration in education. By mapping each variable to its theoretical basis  
and citing seminal as well as contemporary works, the table provides a structured overview of the conceptual  
scaffolding that supports the research design.  
Table 2: Measurement Foundations by Construct  
Variable  
Grounded in  
Primary Theoretical Sources  
Co-Planning (CP)  
Davis (1989); Venkatesh et al. (2003);  
Zhang (2025); Magrill et al. (2024)  
Technology  
Acceptance  
Model  
(Perceived Usefulness, Efficiency)  
Instructional Design and Curriculum  
Development literature  
AI-assisted  
research  
instructional  
design  
2. Co-Instruction (CI)  
3. Co-Assessment (CA)  
Dwivedi et al. (2023); Neupane et al.  
(2024); Jamie et al. (2024)  
Personalized learning theory  
Intelligent tutoring systems  
AI-supported learning engagement  
Automated feedback systems  
Formative assessment theory  
Human-AI co-grading research  
Xiao et al. (2024); Black and Wiliam  
(1998); Shubhanshi (2025)  
4. Pedagogical Implementation  
Effectiveness (PIE)  
Shubhanshi (2025); Kotsis (2025);  
Chaves et al. (2025)  
Teacher-in-the-Loop  
framework  
(TiTL)  
Complementary Strengths Model  
Constructivist pedagogy  
Responsible AI frameworks  
AI fairness and bias research  
Educational data governance  
IS Success Model  
Teaching effectiveness scales  
Student engagement frameworks  
Digital transformation literature  
5. Ethical Governance  
Readiness (EGR)  
Dwivedi et al. (2023); Franco et al.  
(2024); Joon et al. (2024)  
6. Outcome Constructs (IO,  
SO, InstO)  
DeLone and McLean (2003); Fredricks  
et al. (2004); Venkatesh et al. (2003)  
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Survey Instrument  
The proposed survey instrument consists of 37 measurement items representing eight latent constructs derived  
from the conceptual framework. Consistent with established procedures for scale development, the measurement  
items were adapted, modified, or developed by the authors based on relevant theoretical foundations and  
empirical studies identified during the document analysis. Existing validated constructs were adopted where  
applicable, while new items were developed to operationalize the novel concept of Generative AI as a Co-  
Instructor, particularly for dimensions that have not yet been empirically measured in prior literature. All items  
are designed to be measured using a five-point Likert scale ranging from 1 (Strongly Disagree) to 5 (Strongly  
Agree). Future empirical studies should establish the instrument's content validity, construct validity, convergent  
validity, discriminant validity, and reliability through expert review, pilot testing, confirmatory factor analysis  
(CFA), and structural equation modeling (SEM).  
Co-Planning (CP)  
The extent to which Generative AI supports curriculum design, lesson planning, instructional resource  
development, and instructional preparation. Developed by the authors based on the Technology Acceptance  
Model (Davis, 1989), Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2003),  
instructional design literature, and recent studies on AI-assisted curriculum development (Magrill et al., 2024;  
Zhang, 2025).  
Item  
CP1  
CP2  
CP3  
CP4  
CP5  
Statement  
GenAI helps me design lessons more efficiently.  
The AI-generated materials align with my course learning objectives.  
Using GenAI reduces the time I spend creating instructional content.  
GenAI improves the quality of my teaching materials.  
GenAI assists in organizing and structuring course topics effectively.  
Co-Instruction (CI)  
The extent to which GenAI enhances instructional delivery, personalized learning, learner engagement, and  
adaptive instructional support. Adapted and modified from literature on intelligent tutoring systems, personalized  
learning, AI-supported learning environments, and student engagement (Fredricks et al., 2004; Dwivedi et al.,  
2023; Neupane et al., 2024).  
CI1  
CI2  
CI3  
CI4  
CI5  
GenAI provides effective clarification of complex concepts.  
GenAI supports personalized learning for students.  
Students receive timely assistance through AI tools.  
GenAI enhances student engagement in my course.  
AI-assisted tutoring improves students’ understanding of course content.  
Co-Assessment (CA)  
The extent to which GenAI supports assessment design, grading, formative assessment, and feedback processes.  
Adapted and modified from Formative Assessment Theory (Black and Wiliam, 1998) and recent research on AI-  
assisted assessment and human–AI co-grading (Shute, 2008; Xiao et al., 2024).  
CA1  
CA2  
CA3  
CA4  
CA5  
GenAI enables faster feedback delivery to students.  
AI-generated feedback is consistent and structured.  
GenAI assists in developing effective grading rubrics.  
AI tools improve formative assessment practices.  
Using GenAI reduces my grading workload.  
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Pedagogical Implementation Effectiveness (PIE)  
The extent to which GenAI is implemented using structured pedagogical practices characterized by human  
oversight, instructional alignment, and responsible integration. Developed by the authors based on the Teacher-  
in-the-Loop (TiTL) framework, complementary human–AI collaboration models, constructivist pedagogy, and  
Responsible AI implementation frameworks (Shubhanshi, 2025; Kotsis, 2025; Chaves et al., 2025).  
PIE1  
PIE2  
PIE3  
PIE4  
PIE5  
I carefully review and validate AI-generated outputs before using them.  
GenAI is used as a supplement rather than a replacement for my teaching.  
AI tools are aligned with my pedagogical approach.  
I maintain control over instructional decisions when using AI.  
AI-generated responses are grounded in course-specific materials.  
Ethical Governance Readiness (EGR)  
The extent to which institutional policies, governance mechanisms, and ethical safeguards support responsible  
AI implementation in higher education. Developed by the authors based on Responsible AI Governance  
literature, AI ethics frameworks, educational data governance, and institutional AI readiness models (Floridi et  
al., 2018; OECD, 2019; Dwivedi et al., 2023; Franco et al., 2024).  
EGR1  
EGR2  
EGR3  
EGR4  
EGR5  
My institution has clear policies regarding AI use in teaching.  
There are safeguards to protect student data when using AI tools.  
Measures are in place to monitor potential AI bias.  
AI use does not undermine teacher professional autonomy.  
Students are guided to use AI responsibly and ethically.  
Instructor Outcomes (IO)  
The perceived impact of GenAI integration on instructor effectiveness, instructional innovation, professional  
productivity, and teaching satisfaction. Adapted and modified from the Information Systems Success Model  
(DeLone and McLean, 2003), technology acceptance research, and studies examining instructional effectiveness  
and AI adoption in education (Venkatesh et al., 2003; Holmes et al., 2022).  
IO1  
IO2  
IO3  
IO4  
GenAI improves my teaching efficiency.  
GenAI enables me to focus on higher-order pedagogical activities.  
My instructional innovation has increased due to AI use.  
I feel empowered rather than replaced by AI tools.  
Student Outcomes (SO)  
The perceived impact of GenAI integration on student engagement, academic performance, self-directed  
learning, and critical thinking. Adapted and modified from Student Engagement Theory (Fredricks et al., 2004),  
adaptive learning literature, and AI-enhanced learning research (Zawacki-Richter et al., 2019; Kasneci et al.,  
2023).  
SO1  
SO2  
SO3  
SO4  
Students demonstrate improved academic performance.  
Students are more engaged in learning activities.  
Students show greater self-directed learning behaviors.  
Students demonstrate stronger critical thinking skills.  
Institutional Outcomes (InstO)  
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The institutional-level impact of GenAI implementation on digital transformation, instructional quality,  
organizational innovation, and sustainable educational development. Developed by the authors based on the  
Information Systems Success Model (DeLone and McLean, 2003), digital transformation literature, and  
Responsible AI implementation frameworks (UNESCO, 2023; Dwivedi et al., 2023; Raisch and Krakowski,  
2021).  
InstO1  
InstO2  
InstO3  
InstO4  
AI integration supports scalable personalized education.  
AI use contributes to digital transformation in the institution.  
Teaching quality has improved due to AI-supported practices.  
AI implementation is aligned with ethical standards.  
Expected Contributions  
Theoretical Contribution  
This study contributes to the emerging scholarship on artificial intelligence in education by conceptualizing  
generative AI as a pedagogical collaborator rather than merely a technological tool. The proposed framework  
integrates insights from educational technology, information systems success theory, student engagement  
research, and responsible AI governance into a unified model.  
Methodological Contribution  
The study develops a 37-item measurement instrument across eight latent constructs, providing a structured tool  
for empirical investigation of human–AI instructional collaboration using structural equation modeling.  
Practical Contribution  
The framework offers guidance for higher education institutions seeking to integrate generative AI into teaching  
practices while maintaining ethical governance, instructor autonomy, and pedagogical integrity.  
Research Limitations  
As a conceptual study, this research does not empirically validate the proposed framework or measurement  
instrument. The proposed constructs and indicators are derived from theoretical synthesis and document analysis  
rather than data collected from higher education stakeholders. Consequently, the findings should be interpreted  
as a theoretical foundation for future empirical investigation rather than definitive evidence of causal  
relationships. Future studies should validate the proposed framework across diverse institutional, disciplinary,  
and cultural contexts.  
CONCLUSION  
This study advances the discourse on transformative technologies in higher education by conceptualizing  
Generative Artificial Intelligence (GenAI) as a co-instructor rather than merely a productivity tool. By  
integrating theoretical foundations from technology acceptance, information systems success, student  
engagement, formative assessment, and responsible AI governance, the study proposes a multidimensional  
framework that situates GenAI within structured pedagogical and ethical boundaries. The framework articulates  
three core instructional domains—co-planning, co-instruction, and co-assessment—supported by pedagogical  
implementation effectiveness and moderated by ethical governance readiness, thereby offering a holistic model  
for sustainable and personalized AI integration.  
The primary contribution of this research lies in its dual focus on conceptual clarity and measurement  
formulation. The development of an eight-construct model and a 37-item Likert-scale instrument provides a  
systematic foundation for future empirical validation using structural equation modeling. By operationalizing  
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GenAI as a collaborative instructional partner, the study shifts the narrative from fragmented adoption toward  
strategic, accountable, and pedagogically grounded implementation.  
Importantly, the findings underscore that the transformative potential of GenAI depends not solely on  
technological sophistication but on structured human oversight, institutional governance, and alignment with  
personalization goals. Sustainable academic communities are forged when AI systems enhance, rather than  
replace, human instructional judgment and when ethical safeguards ensure transparency, equity, and  
accountability.  
So, this conceptual framework offers a robust theoretical and methodological basis for examining human–AI  
instructional collaboration in higher education. Future empirical studies should validate and refine the proposed  
model across diverse institutional and cultural contexts to determine its generalizability and long-term impact  
on personalized learning ecosystems.  
Future research should prioritize validating the proposed 37-item measurement instrument using pilot testing,  
confirmatory factor analysis, and structural equation modeling. Cross-cultural and cross-disciplinary  
investigations will further establish the framework's generalizability and refine its application across diverse  
higher education contexts.  
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