Assessment of Artificial Intelligence-based digital learning systems in higher education amid the pandemic using Analytic Hierarchy

Assessment of Artificial Intelligence-based digital learning systems in higher education amid the pandemic using Analytic Hierarchy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessment of Artificial Intelligence-based digital learning systems in higher education amid the pandemic using Analytic Hierarchy Vikrant Vikram Singh, Nishant Kumar, Shailender Singh, Meenakshi Kaul, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3828524/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract The devastating effects of the 2020 worldwide COVID-19 virus epidemic prompted widespread lockdowns and restrictions, which will continue to be felt for decades. The repercussions of the pandemic have been most noticeable among educators and their students, which boosts the effectiveness of various AI-based learning systems in the education system. This study examines the AI-based digital learning platforms in higher education institutions based on various characteristics and uses of these systems. Several significant aspects of AI-based digital learning systems were obtained from the available literature, and significant articles were selected to properly examine various characteristics and functions of AI-based digital learning platforms used by multiple higher education institutions. The analytical hierarchy process (AHP) is employed to rank multiple AI-based learning systems based on key factors and their sub-factors. The study's outcome revealed which AI systems are effectively used in developing digital learning systems by various higher education institutions. Teaching/learning strategies Data science applications in education Evaluation methodologies Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction As online learning has become more prevalent in higher education in recent years, artificial intelligence (AI) has opened up new avenues for improving teaching and learning in online higher education. However, literature reviews that focus on the functions, effects, and implications of applying AI in the context of online higher education are lacking. Furthermore, it remains unclear which AI algorithms are commonly used and how they will affect online higher education (Ouyang et al., 2022 ). AI has impacted every aspect of our lives, including education. This study uses social network analysis and text-mining techniques to evaluate AI studies in education over 50 years (1970–2020) (Bozkurt et al., 2021 ). Over the past few years, there has been much research interest in applying AI in various fields such as medicine, finance, and law. A recent research focus has been on the potential applications of AI in education; therefore, a systematic review of the literature on AI in education is needed (Salas-Pilco & Yang, 2022 ). The study focused on using AI and its impacts on administration, instruction, and learning. It was built around a narrative and framework for evaluating AI discovered during early investigation. Computers, machines, and other artifacts now exhibit human-like intelligence defined by cognitive capacities, learning, adaptability, and decision-making capabilities thanks to the field of research known as artificial intelligence and the inventions and developments that have followed. These platforms have helped teachers improve the quality of their instructional activities and carry out other administrative tasks, such as reviewing and grading students' assignments, more quickly and effectively. On the other hand, because the systems use machine learning and adaptability, the curriculum and content have been individualized and customized to meet the needs of the students. This has encouraged uptake and retention, which has improved the learning experience for students as a whole (Chen et al., 2020 ; Rosli et al., 2022a ). Higher education has found AI to be a helpful learning tool. It enables teachers to comprehend students' learning contexts better and develop their instructional tactics while also assisting learners in achieving positive learning outcomes in their learning environment. Engineering is where artificial intelligence is most frequently used (including computer courses). In higher education, AI technology is most frequently used for profiling and prediction, followed by intelligent tutoring systems, grading, and scoring (Chu et al., 2022 ). The most commonly mentioned study questions include learning behavior, accuracy, sensitivity, precision, cognition, and impact. There is a dearth of literature about high-level cognitive abilities, teamwork or communication, self-efficacy or confidence, and learner skills in higher education. Education is one of the many industries implementing AI. AI is mainly used in education for tutoring and assessment purposes. The studies that have been examined make it abundantly evident that most of them do not reflect the pedagogy underlying educational action. The primary application of AI appears to be formative assessment. The automatic grading of learners is one of the key uses of AI in assessment. The contrasts between the usage of AI and its non-use are analyzed in several researches (González-Calatayud et al., 2021 ). The ethical and sustainable development of artificial intelligence depends on good governance. Governments, research institutions, and businesses in China have released ethical standards and principles for AI and started efforts to create AI governance technologies that are helpful to human society (Wu et al., 2020 ; Rosli et al., 2022b ). Although references to AI in the literature are frequently ambiguous and subject to disagreement, AI has substantial implications for higher education. The Discourse of Imperative Transformation describes how AI is viewed as an unavoidable change everyone must adapt. The Discourse of Altering Authority also explains how texts portray AI as degrading the teacher and distributing authority among staff, machines, companies, and students (Bearman et al., 2022 ). Similar practices are introduced by the majority of governments and educational regulatory bodies around the world. In the absence of sound ethical standards and principles, smooth implementation of AI is not possible in education sector as it is a very sensitive area. AI-based systems have many application areas like education, manufacturing, service delivery, medical science, etc. Many researchers have studied the uses of AI in various domains of medicine, such as facial imaging, breast imaging, and plastic surgery (Mendelson, 2019 ). AI provides educators with a number of chances for better lesson design (e.g., identifying and familiarising educators with students' requirements), execution (e.g., quick feedback and teacher intervention), and assessment (e.g., automated essay grading). Different responsibilities for teachers play in the advancement of AI technology. These jobs include serving as role models for AI algorithm training and participating in AI development by verifying the precision of AI automated evaluation systems (Celik et al., 2022 ). As AI is widely used by academic institutions throughout the world, it is imperative to analyze the effectiveness of AI in educational institutions in terms of the impact of AI on learning achievement and the learning perception of various students and educators in these institutions. Some researchers suggested that AI strongly affects the learning achievement of learners through many advanced features and applications, but when it comes to the learning perception of users, AI does not much affect conceptual ideologies and perceptions (Zheng et al., 2021 ). AI-based learning systems are beneficial to higher education institutions in achieving competitive advantage. Various machine learning and AI applications help these institutions gain an edge over their competitors (Zawacki-Richter et al., 2019 ). These institutions can achieve higher satisfaction of various stakeholders through the optimal use of these technologies in their overall system. Ease of use can be achieved by using these technologies in various administrative operations. Better quality and efficient learning can be provided to the students by using multiple AI applications in learning management systems. Institutions can also resolve different quarries of the students and deliver results and feedback to the students on a timely basis by using these applications of AI and machine learning (Hannan & Liu, 2021 ). The way individuals live in society is changing due to AI. AI-powered new technologies have been implemented in various economic sectors, and the educational setting is no exception. AI has been seen as essential to creating learning strategies, particularly in distant learning. Studies reveal that important issues like instructors' employability, technological training, or the moral ramifications of employing AI in education receive little attention despite the expanding use of AI in online learning (Durso & Arruda, 2022 ). One cannot imagine an effective and robust distance learning model without using AI and machine learning applications. The problem of lack of physical interaction and face-to-face learning can be overcome to a considerable extent with various machine learning applications. The use of AI and machine learning-enabled technologies in the education system is increasing day by day (Mairal, 2022 ). The last few decades have witnessed the use of technology in the education system, which has become quite helpful and lifesaving during the COVID-19 pandemic. In the difficult time of the pandemic, technology-based infrastructure equipped with AI support gave new hope to the education system. Universities and institutions kept providing quality education to their students through this platform. The role of these new technologies will be even more in a post-pandemic environment. Now, people have gone through a wonderful experience with a technology-equipped education system and its advantages, so they will be expecting more from institutions regarding educational offerings through these advanced systems (Incio Flores et al., 2021 ). Academic institutions are adopting many AI-based intelligent tutoring and natural language processing systems through advanced AI-based algorithms. These systems are advantageous in evaluating the learning anxiety of students, which can further help them develop the confidence to learn new things. These systems are also used to address issues like the willingness to communicate by students, the knowledge acquisition capabilities of learners, and the level of classroom interaction in various classes conducted by different educators in the institute (Liang et al., 2021 ). Overall, these AI-based systems can help improve teaching and learning systems in educational institutions. RQ 1- What are the key enablers of assessing AI-based digital learning systems? The authors have dealt with ambiguous and incomplete data to answer the above RQ. The authors have also performed an extensive literature review and rigorous investigation of previous studies. Understanding these studies has helped identify the key enablers of assessing AI-based online learning platforms. Various criteria and capabilities (sub-criteria) for an AI-based online learning system have been determined by analyzing the available literature. RQ2- What are the uses of AI-based digital learning systems in higher education institutions? To address RQ-2, further study of the previous literature has been conducted. Previous studies in the field led us to identify key uses of AI-based online learning systems for various higher education institutions. The authors observed and presented that AI-based learning systems are useful for effective content delivery, development, research & innovation, and feedback management. RQ-3: How can the relative and overall importance of these criteria and enablers be assessed, and what priority of focus is established for the current theme in assessing AI-based digital learning systems in higher education institutions? To acknowledge the RQ 3, the current study has performed a step-wise methodology consisting of three steps. In the first step, the identification of the enablers is done by the authors. This is done by circulating a research questionnaire to collect experts' data and then assessing the enablers based on the data quantitatively using the Pythagorean fuzzy Delphi (PF-Delphi) method. In the Second step, the selected enablers are then ranked using a combined framework of the Pythagorean fuzzy analytic hierarchy process (PF-AHP) technique and the Pythagorean Fuzzy- Delphi (PF-D) method in the third step. A sensitivity analysis is then performed to validate the results. 2 Literature Review This section contains the required literature on assessing AI-based digital learning systems in higher education frameworks, the importance of AI-based online learning systems, definitions of the deduced criteria, theoretical information of the enablers, and research contributions made by this study. It also highlights ethical issues that might be faced by education institutions while implementing AI-based online learning systems. 2.1 Appropriate article selection for assessing AI-based digital learning systems It was imperative to review existing literature and identify the content that can help us address the research problem in the current study. For this purpose, relevant articles were searched through various key research databases like Scopus, Google Scholar, etc. This article search process aimed to select quality research papers relevant to our study. Some of the keywords that were used in this search process are "AI-based learning systems," "Online learning systems," "Knowledge management system," "AI in digital learning," and "learning management systems in higher education," etc. These selected articles outlined various key aspects of AI-based learning systems used in higher education systems. When deciding which AIEd technologies to implement, HEIs must make an increasingly significant financial choice (Wu W. et al., 2020 ). The ethical framework and standards implemented by educational authorities concerning the technological development of artificial intelligence and other related technologies have become essential in various areas of society. AI benefits human societies, governments, research institutions, educational institutions, and companies only if it is implemented through proper ethical standards and practices. Without ethical standards, it may create various harms to society. The study suggested that education institutions can achieve better communication among stakeholders, delivery accuracy, and impact by using AI in online learning management systems and MOOC courses. The most profound changes are anticipated to be brought about by AIEd technology in the field of teaching and learning (Chu H C et al., 2022). Predictions of learners' learning status (including dropout and retention, student models, and academic achievement) through profiling are most frequently discussed in AI in higher education studies. The use of AI technologies in higher education has various beneficial effects, such as encouraging active learning, student engagement, and satisfaction, as well as perhaps improving learning communities and lowering feelings of isolation(Celik et al., 2022 ). The most recent advancements in AI research and practice in higher education are essential for practitioners and researchers. Teachers must comprehend the part AI technology can play in the teaching and learning process and how to apply it to help students. Educational institution leaders must comprehend teachers' difficulties while implementing AI technology in their classrooms. 2.2 AI-based digital learning systems in Higher Education A systematic review of selected articles identified various key applications and uses of AI-based online learning platforms, which could be selected as enablers for assessing AI-based online learning systems in higher education institutions. AI can potentially help accelerate research in language studies (Liang et al., 2021 ). Technology-based learning review models are employed to assess the role of AI in language education. Writing, reading, and vocabulary acquisition are three key application areas of AI in language education, which are delivered through various course management systems and learning content management systems. According to the literature, many AI-based algorithms help learn anxiety, willingness to communicate, knowledge acquisition, and classroom interaction in institutions providing language education. Institutions are not using AI-based technological infrastructure to improve areas like higher-order thinking, complex problem-solving, critical thinking ability, and collaborative learning tendencies in language education. The use of AI technologies such as machine learning, deep learning, and natural language processing through learning management systems and virtual learning environments in the digitization of the educational process is increasing day by day (Salas-Pilco et al., 2022). Studies revealed that the identified AI applications are very useful in addressing key issues related to higher education, such as profiling of students, probability of student dropout, dissatisfaction among students, etc., which is very useful in designing a compact system of delivering quality education to overcome such challenges of the education system in today's highly competitive environment. Bozkurt A. et al. ( 2021 ) identified two key technological areas of online education that are positively impacted by artificial intelligence. These areas are pedagogical and technological issues related to online education platforms. The study suggested five key broad research themes related to the online education environment. AI-enabled education (AIEd) is also anticipated to enable more individualized instruction and shift away from a one-size-fits-all approach to instruction and evaluation. According to the existing literature in this area, deep learning and machine learning algorithms for online learning processes are implemented by many successful institutions for better development and delivery of content. Educational use of AI-generated data and Educational human-AI interaction is used to make the system more interactive and user-friendly. Adaptive learning and personalization of education through AI-based practices are also done through various tools of machine learning and other technological innovations, and the use of AI in higher education has become widespread among institutions throughout the world. The study also highlighted how most institutions ignore the ethical concern of AI while implementing it in their online education system (Gonzalez C. V. et al., 2021). AI-based pedagogy underlying the educational action is not reflected in the case of many institutions. Also, AI is mainly used for formative evaluation by institutions. Institutions are also using AI-based supporting systems to grade various assignments and exams conducted by institutions automatically. Studies revealed that when it comes to the actual implementation of AI, institutions are not using it for educational reforms, which can be a game changer if used properly. They are using it for technological advancement and automation of the teaching administration process. Studies also emphasized the need for teacher training to implement these AI-based systems better. Chen LJ et al. (2020) highlighted many key changes and reforms brought up by AI in the education sector. They suggested that education institutions are using various AI-based systems in different forms on an extensive basis. In the initial stage, institutions started using computer-based applications of AI; gradually, they shifted to web-based AI frameworks and started using online intelligent education systems for online content delivery. They pointed out that recent advancements in AI have given education institutions opportunities to use embedded computer systems to combine AI with other technologies to offer hominoid robots and AI-based chat boxes in virtual learning environments and even metaverse technology in the future. All these applications are beneficial in administrative support, evaluation, content delivery, communication, feedback, and other areas of the education system (Mendelson E B, 2019). AI-based systems are exact and sensitive to information; at the same time, ease of use is also a vital feature of these advanced systems, making them even more applicable in various domains. 2.3 Identification of enablers for assessing AI-based digital learning systems Implications of integrating online learning and AI-enabled tools are essential for better content delivery, the importance of AI-enabled technology for processing real-time educational data, and the far-reaching impact of AI applications in online higher education (Ouyang et Al., 2022 ). Traditional AI technology is highly used in online teaching platforms, whereas more advanced technologies like genetic algorithms and deep learning are rarely used in the online IT-enabled infrastructure of higher education. Bearman M. et al. ( 2022 ) conducted a disclosure analysis of including text using AI to critically review the discourses of artificial intelligence in higher education to understand how to progress AI-related research and analysis. This study addressed a few confusing references related to AI as a research tool in higher education. The actual meaning and use of AI are not clear to some institutions, and they are not much aware of the far-reaching applications of AI in research and analysis of educational data through learning content management systems. In academic institutions, AI is presented as an inventible change for which all stakeholders must be prepared. This representation has a negative impact on the acceptability and adaptability of AI among teachers, students, management, and other stakeholders. Artificial intelligence is a tool of competitiveness in higher education (Hannan, E. & Liu, S.,2021). Few studies analyzed various AI-based online learning systems used in multiple higher institutions and how these tools are helping these institutions increase competitiveness and gain competitive advantage over competitors. These studies suggested future direction for higher institutions using AI-enabled tools in their system. AI applications like learning management systems and course management systems are helping institutions in three main areas: automation of administrative operations, digitization of student learning systems, and software support to analyze student results and student support systems. The use of AI in various aspects of distance learning and human interaction is much less than in regular teaching mode (S. D. & Arruda, E. P., 2022). Students may need to trust the technology and think that the data supplied by a system is accurate and dependable for AIEd technologies to be wholly accepted and utilized. Educators need to use AI-based methods effectively to provide a real-time environment and to remove the barriers of contactless learning of distance learning systems. New technologies powered by AI have made distance learning much more realistic and comparative. The study also revealed that through various applications of AI, like virtual learning environments and user management systems, these institutions can offer multi-level support to instructors and learners in distance learning systems through the far-reaching impact, precision, and cognition (knowledge processing) characteristics of these systems. Training requirements and employability of teachers are two key areas of concern in the distance learning education model. Although technology has always been crucial to higher education, it is being used more frequently than ever because of the popularity of smart devices and online courses. There are several ways artificial intelligence is being utilized to support student learning as it becomes more prevalent in higher education. The field of artificial intelligence in education (AIED) has existed for more than 60 years. We will benefit from artificial intelligence. Indeed, AI will also change higher education in the future (Flores, FAI., et al., 2022). AI has gradually contributed to the education system from time to time to reach the current level where people sitting in any part of the world can communicate and be enlightened and educated through various modes of teaching equipped with technology through advanced classroom management systems. The role of AI in education has increased drastically over the last 5–6 years; during COVID, the use of AI and other related technologies has empowered the education system to a new level through offerings like massive open online courses and classroom management systems. The role of AI in the education system will be more challenging and crucial after COVID-19. Machine learning and other AI-enabled tools can ease the work of educators in the digital learning environment by saving their time and effort and facilitating better delivery of their content, but on the other hand, it can be a severe threat to their role and existence (Celik, I. et al., 2022 ). AI can help educators better plan their syllabus, inculcating advanced learning behavior among students developing self-efficacy in learning systems, content delivery, and student assessment. In the implementation phase also, they can use AI to take real-time feedback from students, and they can make content more presentable and application-oriented through various applications of AI like virtual learning environments and course management systems. However, implementing AI in the education system has made their job much more challenging. Now, they need to have awareness and knowledge of these new technologies and their application to use them in their teaching and learning systems effectively. Zheng, LQ et al. (2021) conducted a meta-analysis on the effectiveness of AI for academic institutions in terms of positive change in their learning outcome and learning assessment through various applications of AI, like learning content management systems, learning management systems, and course management systems. AI is widely used in academic institutions worldwide, but no research has been conducted so far on quantitative assessment of the effectiveness of AI in shaping learning achievement and learning perception in these educational institutions. AI has a powerful impact on the learning achievements of educational institutions through various uses like intelligent teaching, automotive feedback, and profiling, but it has a weak effect on the learning perception of multiple students and educators in these educational institutions. Table 1 presents various criteria and their sub-criteria (identifiers), which were selected based on a detailed analysis of key literature available in the study area. As shown in Table 1 four key criteria- Uses in online teaching, uses in R & D of learning platform, uses in review & feedback of learning platform, and characteristics of AI-based learning platforms are employed in previous studies to assess AI-based online learning platforms. Table 1 Criteria for assessing alternative AI-based digital learning systems Sr. No. Criteria Sub criteria Abbreviations Contributors 1 Uses in Online Teaching Intelligent Tutoring UIT Chen LJ et al. (2020), Flores, FAI. et al (2022), Zheng, LQ et. al (2021) Automotive Grading UAG Gonzalez C. V. at al (2021), Chen LJ et al (2020) Formative Assessment UFA Gonzalez C. V. at al (2021), Chen LJ et al (2020), Zheng, 2 Uses in R & D of learning platform Research & Analysis UR&A Chu H. C. et al. (2022), Bearman M. et al. ( 2022 ) Pedagogy Development UPD Bozkurt A. et al ( 2021 ), Celik, I. et al ( 2022 ) Content Development UCD Bozkurt A. et al ( 2021 ) 3 Uses in Review and Feedback of Learning Platform Profiling UP Salas-Pilco et al (2022), Chu H. C. et al (2022), Zheng, LQ et. al (2021) Automotive Feedback Management UAFD Celik, I. et al ( 2022 ), LQ et. al (2021) Policy Implementation UPI Chu H. C. et al. (2022) 4 Characteristics of AI-based learning platform Learning Behavior CLB Celik, I. et al ( 2022 ) Accuracy CA Wu W. et al ( 2020 ), Celik, I. et al ( 2022 ) Sensitivity CS Mendelson E B (2019) Precision CP Mendelson E B (2019), Durso, S. D. & Arruda, E. P. (2022) Cognition CC Durso, S. D. & Arruda, E. P. (2022) Impact CI Durso, S. D. & Arruda, E. P. (2022) Communication CCO Bozkurt A. et al ( 2021 ), Wu W. et al ( 2020 ), Chen LJ et al (2020), Flores, FAI. et al (2022) Self-efficacy CSE Celik, I. et al ( 2022 ) Ease of Use CEU Mendelson E B (2019) Source : Author's computation These criteria are further subdivided into various capabilities to analyze the detailed impact of each criterion in assessing AI-based online learning systems. Table 2 List of AI-based Digital Learning Systems used in higher education institutions SN. AI-Based Online Learning Systems Abbreviations Contributors 1. Learning Management System LMS Salas-Pilco et al (2022), Wu W. et al ( 2020 ), Hannan, E. & Liu, SG. (2021), Zheng, LQ et al. (2021) 2. Learning Content Management System LCMS Bearman, M. et al. ( 2022 ), Chu H. C. et al. (2022), Zheng, LQ et al. (2021), Liang, JC et al. (2021) 3. Classroom Management System CMS Flores, FAI. et al (2022), Flores, FAI. et al. (2022), 4. Virtual Learning Environment VLE Salas-Pilco et al (2022), Chen LJ et al (2020), Durso, S. D. & Arruda, E. P. (2022), Celik, I. et al ( 2022 ) 5. Course Management System COMS Hannan, E. & Liu, SG. (2021), Celik, I. et al ( 2022 ), Zheng, LQ et. al (2021), Liang, JC et. al. (2021) 6. User Management System UMS Hannan, E. & Liu, SG. (2021), Durso, S. D. & Arruda, E. P. (2022) 7. Intelligent Education System/ Supporting Systems IES/SS Chen LJ et al. (2020) 8. Massive Open Online Courses MOOC Wu W. et al. ( 2020 ), Flores, FAI. et al (2022) Source : Author's computation Table 2 shows various AI-based digital learning systems used by multiple educational institutions to achieve different objectives. There are a total of 8 types that various education institutions commonly use in their online learning systems. Each of these systems is useful in different ways. 2.4 Articulation of theoretical framework A review of the literature conducted in this study also targeted establishing theoretical linkage for addressing key research questions raised in this study. Through a thorough examination of existing literature, theoretical linkages were established for solving the research problems of the current study. Customer satisfaction index model (ACSI) is a cause-effect model that says that satisfaction is caused by each factor involved as an input. This theory is expanded in the education sector to study the usefulness and effectiveness of various AI-based online learning platforms based on various characteristics like ease of use, accuracy, precision, cognition, and self-efficacy of these AI-based systems (Matsatsinis, NF et al., 2003 ). In an e-learning environment, this theory links these characteristics of AI-based digital learning systems used in higher education institutions as key driving forces that affect these systems' usefulness and accountability. Learning condition theory believes that overall learning is affected by internal and external conditions, which consist of various factors. Internal condition refers to factors like self-efficacy, learning behaviors, and cognition. External factors include communication, impact, and sensitivity of AI-based learning. In an e-learning environment, online teaching methods and tools/ software used in an e-learning platform are key external factors affecting profiling, prediction, intelligent tutoring, and formative assessment in AI-based online learning systems (Hwang, G.J. et al., 2022). Social cognitive theory believes that knowledge creation for the student in online learning is a social context because, in this process, students interact with other social elements, engage in various activities, and receive inputs from multiple people. So overall impact of any technological tool for assisting the process of knowledge creation should be measured in terms of the interaction of these entities and the usefulness of that technology to various stakeholders who interact in the process of knowledge creation (Swiecki, Z., et al., 2022 ). The theoretical framework of this study is inspired by social cognitive theory, Fornell's customer satisfaction index model (ACSI), and learning condition theory. This study proposes a hybrid theoretical model combining these three key theories. Various criteria and sub-criteria proposed through the theoretical model are extracted through the interaction of these theories, which are very crucial for the assessment of the use of AI-based digital learning platforms in higher education institutions, which are designed to cope with the challenges of delivering quality education through digital mode in normal time as well as pandemic period. 3 Methods This section discusses how enablers were selected using the specified criteria and then ranks them according to their calculated weightage. Figure 1 shows the research process flow that was used in this study. 3.1 Pythagorean Fuzzy Sets Real-world input data for multi-criteria decision-making (MCDM) are insufficient and ambiguous. Intuitionistic fuzzy sets, which can be expressed as membership functions, non-membership functions, and hesitation degrees, can manage this ambiguity. When membership and non-membership have degrees greater than 1, intuitionistic fuzzy sets cannot consider uncertainty. However, the issue is solved by Pythagorean Fuzzy Sets (PFS), an expansion of intuitionistic fuzzy sets. Introducing Pythagorean Fuzzy Sets (PFS), Yager (2013). Like fuzzy sets, these sets handle selection ambiguity and permit flexible reasoning following human norms. An object called a Pythagorean Fuzzy Set (PFS) P is defined as: $$\text{P}=\{|x\in X\}$$ $${{\mu }}_{\text{p}} \left(\text{x}\right) \in \left[\text{0,1}\right] {{\upvartheta }}_{\text{p}} \left(\text{x}\right)\in [0, 1]$$ $$0\le {{\mu }}^{2}+ {{\upvartheta }}^{2} \le 1$$ Pythagorean Fuzzy Sets can be used to calculate the Degree of Determinacy ( \({\pi }\) ) since they are driven by a membership function ( \({\mu }\) ) and a non-membership function ( \({\upvartheta }\) ). $${\pi }= \sqrt{(1}-{{\mu }}^{2}- {{\upvartheta }}^{2})$$ Overall, membership ( \({\mu }\) ) and non-membership degrees ( \({\upvartheta }\) ) of Pythagorean fuzzy sets can be more than 1, but not their squares. The intuitionistic membership grades are below the line x + y ≤ 1, and the Pythagorean membership grades are with x 2 + y 2 ≤ 1 for all the points (x, y) with both an intuitionistic and a Pythagorean membership grade. The Pythagorean membership grade offers greater flexibility to solve the worry of uncertainties and mistakes in expert input data, as shown by a comparison between the Pythagorean membership grade and intuitionistic membership grade in Fig. 2 . 3.2 Pythagorean Fuzzy- Delphi (PF-D) Pythagorean Fuzzy-Delphi, which combines the classic Delphi method and Fuzzy sets, provides decisions based on human evaluation (Zadeh et al. 1996). The analysis quality has been enhanced using Pythagorean fuzzy sets (Sindhwani et al., 2022). The subsequent steps were performed to apply the method: Step I: Based on the current literature research, the identified criterion and the set of enablers are given in tabular format for expert evaluation. Step II: Expert opinions were expressed in linguistic form and converted to Pythagorean fuzzy numbers using the scale presented in Table 3 . According to A. Kumar et al. (2018), Eq. (1) indicates the evaluation score (Si j) provided by j experts for i enablers: $${\text{S}}_{\text{i}\text{j}}=\left( {{\mu }}_{\text{i}\text{j}, }{{\upvartheta }}_{\text{i}\text{j}}\right) \left(1\right)$$ Step III: The row-wise sets are subjected to a union operation to produce the combined structure required by Eq. (2) (Abdullah & Goh, 2019): $${\alpha }=\left(\text{max}{{\mu }}_{\text{i}\text{j} },\text{min}{{\upvartheta }}_{\text{i}\text{j}}\right) \left( {{\mu }}_{\text{i}}^{{\prime }}, {{\upvartheta }}_{\text{i}}^{{\prime }}\right) \left(2\right)$$ Step IV: Calculate the degree of hesitancy following Eq. (3): $${{\pi }}_{\text{i}}^{{\prime }}=1- {{\mu }}_{\text{i}}^{{\prime }2}- {{\upvartheta }}_{\text{i}}^{{\prime }2} \left(3\right)$$ Step V: Finding a crisp value for each enabler using Eq. (4): $${\text{d}}_{\text{f} }\left({\alpha }\right)= \frac{1+ {{\mu }}_{\text{i}}^{2} -{{\upvartheta }}_{\text{i}}^{2}- {{\pi }}_{\text{i}}^{{\prime }2} }{2} \left(4\right)$$ Table 3 Rating of capability and criteria using Linguistic Terms (Liu et al., 2021) Linguistic Term PFN Perfectly High (PH.) (0.950, 0.200) Very High (VH.) (0.850, 0.350) High (H) (0.700, 0.400) Medium High (MH.) (0.650, 0.450) Average (A) (0.500, 0.550) Medium Low (ML) (0.400, 0.650) Low (L) (0.350, 0.750) Very Low (VL.) (0.250, 0.850) Very Very Low (VVL) (0.200, 0.950) Source : Author's computation 3.3 Pythagorean Fuzzy -Analytic Hierarchy Process (PF-AHP) In contrast to other knowledge-based approaches like ANP, TOPSIS, and ELECTRE, AHP provides a superior way of resolving MCDM issues and communicating efficient outcomes (Keshavarz Ghorabaee et al., 2017). The PFN-AHP technique to deal with ambiguity and imprecision assigns a relative relevance score and integrates with conventional AHP. To include PF-AHP in the study, the steps below were taken: Step I: Ilbahar et al. (2018) created the pair-wise comparison matrix \({\text{X}}_{\text{i}\text{j}}=({\text{X}}_{\text{i}\text{j}}{)}_{\text{m}\times \text{m}}\) for each criterion using the linguistic phrases shown in Table 4 as a basis. Step II: Utilising the membership and non-membership functions from equations (5) and (6), compute the difference matrix: $$Lower {d}_{ij}= {\mu }_{ij}^{2}- {\vartheta }_{ij}^{2} \left(5\right)$$ $$Upper {d}_{ij}= {\mu }_{ij}^{2}- {\vartheta }_{ij}^{2} \left(6\right)$$ Step III: Utilizing equations (7) and (8), compute the interval multiplication matrix: $$\text{L}\text{o}\text{w}\text{e}\text{r} {\text{S}}_{\text{i}\text{j}}=\sqrt{ {1000}^{\text{d}} } \left(7\right)$$ $$\text{U}\text{p}\text{p}\text{e}\text{r} {\text{S}}_{\text{i}\text{j}}= \sqrt{{1000}^{\text{d}} } \left(8\right)$$ Step IV: Following Eq. (9), the determinacy value ( \({\tau }\) ) of the \({\text{X}}_{\text{i}\text{j}}\) is calculated as follows: $${\tau }=1-\left({\text{U}\text{p}\text{p}\text{e}\text{r} {\mu }}_{\text{i}\text{j}}^{2}- \text{L}\text{o}\text{w}\text{e}\text{r} {{\mu }}_{\text{i}\text{j}}^{2}\right)- \left({\text{U}\text{p}\text{p}\text{e}\text{r} {\upvartheta }}_{\text{i}\text{j}}^{2}- \text{L}\text{o}\text{w}\text{e}\text{r} {{\upvartheta }}_{\text{i}\text{j}}^{2}\right) \left(9\right)$$ Step V: Determinacy degrees are multiplied with \(\text{S}=({\text{S}}_{\text{i}\text{j}}{)}_{\text{m}\times \text{m}}\) to get a matrix of weights, \(\text{T}={(\text{t}}_{\text{i}\text{j}}{)}_{\text{m}\times \text{m}}\) before normalization using Eq. (10): $${\text{t}}_{\text{i}\text{j}}=\left(\frac{\text{L}\text{o}\text{w}\text{e}\text{r} {\text{S}}_{\text{i}\text{j}}+ \text{U}\text{p}\text{p}\text{e}\text{r} {\text{S}}_{\text{i}\text{j}}}{2}\right) {{\tau }}_{\text{i}\text{j}} \left(10\right)$$ Step VI: Normalized priority weight \({\text{w}}_{\text{i}}\) using Eq. (11): $${\text{w}}_{\text{i}}= \frac{{\sum }_{\text{j}=1}^{\text{m}}{\text{t}}_{\text{i}\text{j}}}{{\sum }_{\text{i}=1}^{\text{m}}{\sum }_{\text{j}=1}^{\text{m}}{\text{t}}_{\text{i}\text{j}}} \left(11\right)$$ Table 4 Scale of relative importance for AHP (Ilbahar et al. 2018) Linguistic Term Pythagorean fuzzy numbers (PFN) µ L µ U νL νU Certainly, Low Importance (CLI) 0.00 0.00 0.90 1.00 Very Low Importance (VLI) 0.10 0.20 0.80 0.90 Low Importance (LI) 0.20 0.35 0.65 0.80 Below Average Importance (BAI) 0.35 0.45 0.55 0.65 Average Importance (AAI) 0.45 0.55 0.45 0.55 Above Average Importance (AAI) 0.55 0.65 0.35 0.45 High Importance (HI) 0.65 0.80 0.20 0.35 Very High Importance (VHI) 0.80 0.90 0.10 0.20 Certainly, High Importance (CHI) 0.90 1.00 0.00 0.00 Exactly Equal (EE.) 0.197 0.197 0.197 0.197 Source : Author's computation 4 Results This work aims to offer a set of evaluation criteria for digital learning systems powered by AI. Relevant criteria needed to be sorted into the appropriate categories to understand future research and development in this field. Additionally, it's crucial to prioritize these facilitators and carry out a quantitative analysis. Given the state of technology today, society and corporations can conduct research and development (R&D) and think about novel ideas. Therefore, expert input from academia, business, and research experts is essential to offer professional judgments and vital insights. Using the snowball sampling method, a panel of six experts was chosen to assess the acceptance of the suggested facilitators. The Table 5 lists the specifics of the selected specialists, their specialties, and the lengths of time they have worked in each subject. The experts' chosen biographical information, including their industry of specialization, educational background, work history, and critical roles, is displayed in the Table below. Table 5 Experts Profile Experts Industry Role Qualification Experience (Years) Expertise E1 EdTech Manager MBA 17+ Emerging technology adoption for enriching learning experience. E2 Higher Education Professor Ph.D 20+ Teaching and research interest revolves around ICT tools and their adoption for better learning experience. E3 Higher Education Professor Ph.D 19+ Works on developing sustainable systems with machine-human connection. Worked on many projects based on sustainability, technology, and education. E4 EdTech Assistant Manager M Tech 14+ Expert in demonstrating the use of AI, ML, and other emerging technologies in simplifying the learning process in education. E5 Management Consulting Research Analyst MBA 10+ Associated with a consulting firm and working on the implication of disruptive innovation in the educational space. E6 Academic Publishing Manager MBA 16+ Proficient in interactive e-content development for a leading publishing group. Source : Author's computation 4.1 Stage I Finalisation of criterion and enablers Four criteria and eighteen capabilities for an AI-based digital learning system have been identified based on an analysis of the available literature. The ambiguity of the option was handled with Pythagorean fuzzy-delphi. A questionnaire about the overall acceptability of ability was supplied to the expert panel. The response was recorded in language terms and subsequently converted to PFNs (Pythagorean fuzzy numbers), as shown in Table 3.1. To discover the crisp values specified in Table 6 in further detail, the obtained PFNs are also defuzzified. Data pertaining to language was processed using the PF-Delphi algorithm. Based on the literature, a threshold value of 0.6 was used to determine if capabilities were accepted or rejected (Shen et al., 2019). Table 6 Pythagorean fuzzy weights and de-fuzzified values Criteria Code Capability µ ϑ π d f (α) Uses in Online Teaching (UOT) UOT1 Intelligent Tutoring 0.950 0.200 0.058 0.930 UOT2 Automotive Grading 0.700 0.400 0.350 0.604 UOT3 Formative Assessment 0.700 0.400 0.350 0.604 Uses in R & D of learning platform (URD) URD1 Research & Analysis 0.700 0.400 0.350 0.604 URD2 Pedagogy Development 0.950 0.200 0.058 0.930 URD3 Content Development 0.850 0.350 0.155 0.788 Uses in Review and Feedback of Learning Platform (URF) URF1 Profiling 0.700 0.400 0.350 0.604 URF2 Automotive Feedback Management 0.850 0.350 0.155 0.788 URF3 Policy Implementation 0.850 0.350 0.155 0.788 Characteristics of AI-based learning platform (CAI) CAI1 Learning Behaviour 0.250 0.850 0.215 0.147 CAI2 Accuracy 0.850 0.350 0.155 0.788 CAI3 Sensitivity 0.250 0.850 0.215 0.147 CAI4 Precision 0.250 0.850 0.215 0.147 CAI5 Cognition 0.850 0.350 0.155 0.788 CAI6 Impact 0.950 0.200 0.058 0.930 CAI7 Communication 0.700 0.400 0.350 0.604 CAI8 Self-efficacy 0.700 0.400 0.350 0.604 CAI9 Ease of Use 0.250 0.850 0.215 0.147 Source : Author's computation Note : Capability with a threshold value greater than 0.6 was retained for the next phase of analysis. 4.2 Stage II Relative weight calculation and ranking The creation of a hierarchy model was the main objective in assembling the expert panel. Three stages in Fig. 3 depict the proposed hierarchy concept. Level I: AI-based learning (the purpose); Level II: Categorising the criteria; and Level III: Evaluating the capabilities of the major criteria. The aforementioned survey data was processed using the PF-AHP algorithm. Based on the specified procedures in Section 3, the difference matrix, interval multiplicative matrix, determinacy value matrix, and normalized priority weight were generated and displayed in Tables 7 and 8 . Table 7 Pair-wise Comparison Decision Matrix for Criteria Criteria UOT URD URF CAI µ L µ U νL νU µ L µ U νL νU µ L µ U νL νU µ L µ U νL νU UOT 0.200 0.350 0.650 0.800 0.450 0.550 0.450 0.550 0.550 0.650 0.350 0.450 0.197 0.197 0.197 0.197 URD 0.197 0.197 0.197 0.197 0.550 0.650 0.350 0.450 0.450 0.550 0.450 0.550 0.650 0.800 0.200 0.350 URF 0.550 0.650 0.350 0.450 0.197 0.197 0.197 0.197 0.350 0.450 0.550 0.650 0.800 0.900 0.100 0.200 CAI 0.350 0.450 0.550 0.650 0.650 0.800 0.200 0.350 0.197 0.197 0.197 0.197 0.650 0.800 0.200 0.350 Source : Author's computation Table 8 Weights before normalization and normalized weight Criteria UOT URD URF CAI Normalized Weight Rank UOT 0.17 0.85 1.69 1.00 0.096 4.00 URD 1.00 1.69 0.85 3.77 0.233 2.00 URF 1.69 1.00 0.43 3.77 0.224 3.00 CAI 0.43 3.77 1.00 9.52 0.327 1.00 Source : Author's computation Similar methods were employed to determine the capability's priority ranking, but this calculation is not displayed due to paper limitations. The results of the PF-AHP method led to the determination of the local and global capability weights. In addition, Table 9 displays each capability's overall ranking as determined by the perspectives of eight experts. The order in which the capability's priority rating was determined is as follows: CAI > URD > URF > UOT. Table 9 Importance weight of capabilities Criteria Criteria weight Capability Code Capability local weight Global weight Global rank UOT 0.104 UOT1 0.334 0.064 3 UOT2 0.153 0.033 13 UOT3 0.165 0.035 12 URD 0.233 URD1 0.285 0.074 1 URD2 0.192 0.051 4 URD3 0.173 0.045 6 URF 0.224 URF1 0.137 0.036 11 URF2 0.286 0.067 2 URF3 0.163 0.032 14 CAI 0.327 CAI2 0.214 0.037 10 CAI5 0.244 0.042 8 CAI6 0.241 0.047 5 CAI7 0.251 0.044 7 CAI8 0.172 0.038 9 Source : Author's computation 4.3 Stage III Sensitivity Analysis Sensitivity analysis is one of the most efficient ways to evaluate a model's reliability and applicability (Abdullah and Goh, 2019). This method is used in the third step to validate the PF-AHP results and test the sensitivity of the used model under constant conditions. Any modifications to the results must be incorporated into the updated framework output (Kumar et al., 2019). The challenges with the highest and second-highest weights were taken into account to apply the technique since it was thought that even small changes in their values would result in significant differences across the rankings of the challenges. Hence, the Characteristics of AI-based learning platform (CAI) was considered. Their weight values varied proportionally from 0.9 times to 0.1 times. For example, the weight of CAI is 0.327, which varies from 0.9*0.327, 0.8*0.327, to 0.1*0.327. The resulting variations in the weights of other challenges are provided in Table 10 . Table 10 Value of main criteria with sensitivity on CAI Criteria Normal 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 UOT 0.104 0.152 0.172 0.196 0.218 0.234 0.244 0.252 0.250 0.250 URD 0.233 0.281 0.301 0.325 0.347 0.363 0.373 0.381 0.379 0.379 URF 0.224 0.272 0.292 0.316 0.338 0.354 0.364 0.372 0.370 0.370 CAI 0.327 0.294 0.262 0.229 0.196 0.164 0.131 0.098 0.065 0.033 Source : Author's computation Corresponding to the variations in CAI values, changes in the capability ranks were also observed, and CAI8 and CAI7 showed major variations. Table 11 shows the variations in capability ranks, and the results are graphically represented in Fig. 4 . Table 11 Variation in capability with sensitivity on CAI Capability Normal 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 UOT1 3 3 3 3 3 3 4 4 4 4 UOT2 13 13 13 13 13 13 13 13 13 13 UOT3 12 12 12 12 12 12 12 12 12 12 URD1 1 1 1 1 2 2 3 3 3 3 URD2 4 4 5 5 4 4 6 6 6 6 URD3 6 6 6 6 6 6 7 7 7 7 URF1 11 11 11 11 11 11 11 11 11 11 URF2 2 2 2 2 5 5 5 5 5 5 URF3 14 14 14 14 14 14 14 14 14 14 CAI2 10 10 10 10 10 10 10 10 10 10 CAI5 8 8 8 8 8 8 8 8 8 8 CAI6 5 5 4 4 7 7 9 9 9 9 CAI7 7 7 9 9 9 9 2 2 2 2 CAI8 9 9 7 7 1 1 1 1 1 1 Source : Author's computation 5 Discussions and Research Propositions This study proposes various theoretical and practical implications that may be adopted by higher education institutions at various levels for enhancing the structure and delivery model of online learning platforms. Proposition 1 Online learning systems must be customized by implementing artificial intelligence to enhance the characteristics of digital learning platforms . AI-based digital learning platforms are reshaping the learning experience in higher education institutions. Adopting AI-based technologies will enhance the various characteristics of digital learning systems (Celik I. et al., 2022 ). Better features would mean more acceptability and reach of these digital learning platforms among students and educators. AI-based learning systems will offer customized learning behavior, higher accuracy, and sensitivity to digital learning systems. The content and process of digital learning will become more impactful, precise, and cognitive. Overall, communication among various users of digital learning platforms will also improve through multiple channels of artificial intelligence (Bozkurt A. et al., 2021 ). Proposition 2 Online teaching in higher education institutions can be enhanced using AI-based digital learning systems. The penetration of AI has changed the traditional model of teaching. Today's teaching has become more output-oriented and informative (Colchester K. et al., 2017 ). AI can scientifically integrate various teaching technologies to make the teaching process systematic and effective. Digital learning systems will integrate features like intelligent tutoring, automotive grading, and formative assessment to enhance higher education institutions' overall teaching and learning ecosystem (Ben Ammar M. et al.,2010). These add-ons will drastically improve the effectiveness and attractiveness of the teaching process. Educators can achieve customized delivery, resource acquisition, individual learning, and talent training through various advanced tools of AI in digital learning environments (Zheng LQ et al.,2021). Proposition 3 Research and development outcomes in higher education institutions can be improved using AI-based digital learning systems. AI has brought advancement in research & development carried out in different areas through many supportive technologies & software designed for conducting qualitative & quantitative research. Remote learning platforms enabled by AI have colossal potential to democratize research & development in higher education institutions by improving the accessibility of research data (Zawacki-Richter et al. ( 2019 ). Researchers worldwide can access quality research data from various research databases and secondary data sources and convert data into multidimensional forms using AI-enabled software and tools. It can also help researchers identify the potential threat of plagiarism in research and act as a warning signal. AI offers many simulation software, data modifiers, analysis tools, and modeling software, which can be very useful to researchers associated with higher education institutions (Franzoni V. et al. ( 2020 ). AI algorithms can also help researchers identify knowledge & research gaps to provide targeted remedial resources in different research fields. Lastly, AI can help researchers form global ties for multidisciplinary and multicontinental research. Through AI-empowered digital learning platforms, content and pedagogy development will become more effective and fruitful (Bearman M et al., 2022 ). Proposition 4 Review and feedback systems in higher education institutions can become more effective and compact by using AI techniques in digital learning systems. Review & feedback is an essential supportive pillars of quality education. It helps learners in achieving learning goals and improving self-regulated skills. The role of review & feedback becomes even more critical in a digital learning environment. Physically and geographically distant instructors and learners can understand each other, communicate with each other, and customize interaction based on review & feedback (Cavalcanti, A. et al.,2021). Through AI-enabled systems, the process of generating, analyzing, and communicating feedback can become time-efficient and effective. AI can even help the instructor collaborate and analyze large amounts of feedback received from multiple sources. Higher education institutions can use AI-enabled online feedback systems to improve student performance and satisfaction (Blikstein et al.,2014). Proposition 5 Adopting the proposed enablers in digital learning platforms will ensure better performance and acceptability of digital learning platforms in higher education institutions. The development of AI is becoming essential for economic sustainability. The education sector is also inseparable from the support of AI in today's digital education environment. Digital learning platforms enabled through AI can be very helpful to various stakeholders of educational institutions (Chu H. et al., 2022). With the developmental role of AI in the online learning environment, education delivery in higher education institutions is developing more and more in the direction of intelligent and informative learning. In a global education environment, AI-based digital learning platforms could be game changers with far-reaching impacts on the outcome of education delivery (Arruda, E. P.,2022). So higher education institutions must integrate AI in their digital learning system to cultivate smart learning, integrate rich and techno-oriented teaching skills, enrich research & development experience, and establish strong feedback & support systems for continuous improvement of the learning system (Bozkurt, A. et al. ( 2021 ). AI must be integrated into the education delivery model of higher education institutions to enhance the reach, connectivity, and productivity of digital learning systems (Zawacki-Richter O. et al., 2019 ). 6 Conclusion Education is one of the industries that offers a vast scope for various applications of AI. In recent years, rapid development and transformation have been witnessed in the education sector through AI. In the digital learning environment, higher education institutions may achieve personalized learning experiences using AI. AI offers many exciting developments for improving higher education worldwide through AI-enabled digital learning systems. AI can significantly enhance the learning process by providing greater precision, technology-empowered teaching, multi-fold uses, and a more accurate review & feedback system. Thus, AI-enabled digital learning systems can help students and instructors develop and implement different learning pathways or provide them with real-time feedback to strengthen the teaching & learning process in higher education institutions. The penetration of AI in digital learning systems has transformed the traditional way of teaching methods and models. Higher education institutions are cultivating general interest, sustainable development, and learning outcomes of students by inculcating AI in their learning model (Chen et al., 2020 ). Various AI tools can meet individual users' expectations and push the optimal digital learning resources, digital learning path, and research & innovation in higher education institutions (Wu et al., 2020 ). AI is a multidimensional collaborator in higher education that has the power to collaborate with multiple domains, geographically diverse users, and globally scattered information through AI-powered digital learning platforms (Zheng et al., 2021 ). Coming-of-age AI and its applications have brought many revolutionary reforms in the higher education system by updating many out-of-date concepts, methods, processes, training systems, feedback systems, and teaching & learning processes (Hannan & Liu, 2021 ). AI has given new wings to digital learning in higher education to bring about lifelong education, borderless learning, smart campus, and smart learning in higher education institutions (Arruda, E. P., 2022). In the present study, authors have covered the majority of execution points addressed by research questions and research objectives by reviewing the most relevant literature and identifying the most impactful criteria and enablers to assess the impact of AI-enabled digital learning platforms in higher education institutions. MCDM through PFS, PF-Delphi, and PF-AHP methods were employed for producing effective results. However, like any other method, these methods have some limitations which may be explored by other researchers. The unbiased opinion of an expert panelist is a must for producing explicit results; thus, any form of bias in judging must be omitted. Despite some limitations, the study has produced useful results in AI-based digital learning systems and created scope for future research in the same area. The output ranking of enablers provides an essential priority list for assessing AI-based digital learning systems, which can be referred by higher education institutions, software developers, and policymakers for designing AI-enabled digital learning systems. Thus, this study gives a clear idea about assessing AI-based digital learning platforms in higher education institutions. Declarations Funding: The authors did not receive any financial support for this research. Conflict of Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Human participants and/or animals: The authors ensure that no animal participation is involved in this research. 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International Journal of Educational Technology in Higher Education , 16 (1), 39. https://doi.org/10.1186/s41239-019-0171-0. Zheng, L., Niu, J., Zhong, L., & Gyasi, J. F. (2021). The effectiveness of artificial intelligence on learning achievement and learning perception: A meta-analysis. Interactive Learning Environments, 1–15. https://doi.org/10.1080/10494820.2021.2015693 . Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 20 Mar, 2024 Reviewers invited by journal 20 Mar, 2024 Editor invited by journal 02 Jan, 2024 First submitted to journal 01 Jan, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3828524","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":281887883,"identity":"65f99bf1-31c9-4a11-a313-233de64e151d","order_by":0,"name":"Vikrant Vikram Singh","email":"","orcid":"","institution":"Symbiosis International (Deemed University)","correspondingAuthor":false,"prefix":"","firstName":"Vikrant","middleName":"Vikram","lastName":"Singh","suffix":""},{"id":281887884,"identity":"b590ccd0-2315-46dd-aee6-1be4b9eea4c8","order_by":1,"name":"Nishant Kumar","email":"","orcid":"","institution":"CHRIST (Deemed to be University)","correspondingAuthor":false,"prefix":"","firstName":"Nishant","middleName":"","lastName":"Kumar","suffix":""},{"id":281887885,"identity":"bc60b222-d64e-4949-bbaf-000dad32f531","order_by":2,"name":"Shailender Singh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYHACNgYGAwkeBgbmAwyMDURrKbCQAdIJpGj5UGHDwMBjQJwW8/b2Z495gA4zOH/mm8TPHTZyDOyHj27Ap0XmzBlzY7CWG7nbJHvPpBkz8KSl3cCnRUIih00aooV3mwRv2+HEBgkeM/xa5J8/k4Y67JnkX6K0SDCYQbQcAFpHnC08OWaSc4BaJG+kGVvLtqUZsxH0C/vxZxJv/tTZ850//PDm2zYbOX72w8fwakEGLBIgko1Y5SDA/IEU1aNgFIyCUTByAAASWUJAgOFsfQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-1710-7504","institution":"Symbiosis International (Deemed University)","correspondingAuthor":true,"prefix":"","firstName":"Shailender","middleName":"","lastName":"Singh","suffix":""},{"id":281887886,"identity":"e239397f-2eea-4fe4-93bb-25dfc2696234","order_by":3,"name":"Meenakshi Kaul","email":"","orcid":"","institution":"Symbiosis International (Deemed University)","correspondingAuthor":false,"prefix":"","firstName":"Meenakshi","middleName":"","lastName":"Kaul","suffix":""},{"id":281887887,"identity":"12da9073-50fd-4165-a28b-a25ed46b77a0","order_by":4,"name":"Aditya Kumar Gupta","email":"","orcid":"","institution":"Amity University Noida","correspondingAuthor":false,"prefix":"","firstName":"Aditya","middleName":"Kumar","lastName":"Gupta","suffix":""},{"id":281887888,"identity":"6bb3973b-8bab-4cad-ac10-1aa352f1df87","order_by":5,"name":"P.K. Kapur","email":"","orcid":"","institution":"Amity University Noida","correspondingAuthor":false,"prefix":"","firstName":"P.K.","middleName":"","lastName":"Kapur","suffix":""}],"badges":[],"createdAt":"2024-01-02 04:11:46","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3828524/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3828524/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53241946,"identity":"aba1cb8f-e299-41f4-9563-2ae57b51f2f5","added_by":"auto","created_at":"2024-03-22 10:13:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":108696,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResearch Process Flow\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3828524/v1/f6a95314f89b514d987d70a6.png"},{"id":53242390,"identity":"4e5d370d-bffa-4454-bbb4-bc412c4d5b80","added_by":"auto","created_at":"2024-03-22 10:21:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":11564,"visible":true,"origin":"","legend":"\u003cp\u003eComparison between Pythagorean membership grade and intuitionistic membership grade. (Source: Yager and Abbassov, 2013)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3828524/v1/d8d25160525a7e6f24e8b634.png"},{"id":53241949,"identity":"f9d98dc0-3c85-47d7-be4d-86e3bfab6887","added_by":"auto","created_at":"2024-03-22 10:13:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":395083,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAHP Model\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3828524/v1/7091040e395144e847b88dca.png"},{"id":53241947,"identity":"b9164d8e-670c-4ea8-8697-a542b53648ba","added_by":"auto","created_at":"2024-03-22 10:13:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":38807,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eGraphical representation of capability variation\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3828524/v1/50a182d8503da3b5dd667d96.png"},{"id":53242914,"identity":"f5bd89ab-d2e1-4577-8e4b-a10f713fb4e3","added_by":"auto","created_at":"2024-03-22 10:29:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":943271,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3828524/v1/671f1348-5c67-4863-b862-9864dbed253a.pdf"}],"financialInterests":"","formattedTitle":"Assessment of Artificial Intelligence-based digital learning systems in higher education amid the pandemic using Analytic Hierarchy","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAs online learning has become more prevalent in higher education in recent years, artificial intelligence (AI) has opened up new avenues for improving teaching and learning in online higher education. However, literature reviews that focus on the functions, effects, and implications of applying AI in the context of online higher education are lacking. Furthermore, it remains unclear which AI algorithms are commonly used and how they will affect online higher education (Ouyang et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). AI has impacted every aspect of our lives, including education. This study uses social network analysis and text-mining techniques to evaluate AI studies in education over 50 years (1970\u0026ndash;2020) (Bozkurt et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOver the past few years, there has been much research interest in applying AI in various fields such as medicine, finance, and law. A recent research focus has been on the potential applications of AI in education; therefore, a systematic review of the literature on AI in education is needed (Salas-Pilco \u0026amp; Yang, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The study focused on using AI and its impacts on administration, instruction, and learning. It was built around a narrative and framework for evaluating AI discovered during early investigation. Computers, machines, and other artifacts now exhibit human-like intelligence defined by cognitive capacities, learning, adaptability, and decision-making capabilities thanks to the field of research known as artificial intelligence and the inventions and developments that have followed. These platforms have helped teachers improve the quality of their instructional activities and carry out other administrative tasks, such as reviewing and grading students' assignments, more quickly and effectively. On the other hand, because the systems use machine learning and adaptability, the curriculum and content have been individualized and customized to meet the needs of the students. This has encouraged uptake and retention, which has improved the learning experience for students as a whole (Chen et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rosli et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHigher education has found AI to be a helpful learning tool. It enables teachers to comprehend students' learning contexts better and develop their instructional tactics while also assisting learners in achieving positive learning outcomes in their learning environment. Engineering is where artificial intelligence is most frequently used (including computer courses). In higher education, AI technology is most frequently used for profiling and prediction, followed by intelligent tutoring systems, grading, and scoring (Chu et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The most commonly mentioned study questions include learning behavior, accuracy, sensitivity, precision, cognition, and impact. There is a dearth of literature about high-level cognitive abilities, teamwork or communication, self-efficacy or confidence, and learner skills in higher education. Education is one of the many industries implementing AI. AI is mainly used in education for tutoring and assessment purposes. The studies that have been examined make it abundantly evident that most of them do not reflect the pedagogy underlying educational action. The primary application of AI appears to be formative assessment. The automatic grading of learners is one of the key uses of AI in assessment. The contrasts between the usage of AI and its non-use are analyzed in several researches (Gonz\u0026aacute;lez-Calatayud et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe ethical and sustainable development of artificial intelligence depends on good governance. Governments, research institutions, and businesses in China have released ethical standards and principles for AI and started efforts to create AI governance technologies that are helpful to human society (Wu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Rosli et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e). Although references to AI in the literature are frequently ambiguous and subject to disagreement, AI has substantial implications for higher education. The Discourse of Imperative Transformation describes how AI is viewed as an unavoidable change everyone must adapt. The Discourse of Altering Authority also explains how texts portray AI as degrading the teacher and distributing authority among staff, machines, companies, and students (Bearman et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimilar practices are introduced by the majority of governments and educational regulatory bodies around the world. In the absence of sound ethical standards and principles, smooth implementation of AI is not possible in education sector as it is a very sensitive area. AI-based systems have many application areas like education, manufacturing, service delivery, medical science, etc. Many researchers have studied the uses of AI in various domains of medicine, such as facial imaging, breast imaging, and plastic surgery (Mendelson, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). AI provides educators with a number of chances for better lesson design (e.g., identifying and familiarising educators with students' requirements), execution (e.g., quick feedback and teacher intervention), and assessment (e.g., automated essay grading). Different responsibilities for teachers play in the advancement of AI technology. These jobs include serving as role models for AI algorithm training and participating in AI development by verifying the precision of AI automated evaluation systems (Celik et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As AI is widely used by academic institutions throughout the world, it is imperative to analyze the effectiveness of AI in educational institutions in terms of the impact of AI on learning achievement and the learning perception of various students and educators in these institutions. Some researchers suggested that AI strongly affects the learning achievement of learners through many advanced features and applications, but when it comes to the learning perception of users, AI does not much affect conceptual ideologies and perceptions (Zheng et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAI-based learning systems are beneficial to higher education institutions in achieving competitive advantage. Various machine learning and AI applications help these institutions gain an edge over their competitors (Zawacki-Richter et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). These institutions can achieve higher satisfaction of various stakeholders through the optimal use of these technologies in their overall system. Ease of use can be achieved by using these technologies in various administrative operations. Better quality and efficient learning can be provided to the students by using multiple AI applications in learning management systems. Institutions can also resolve different quarries of the students and deliver results and feedback to the students on a timely basis by using these applications of AI and machine learning (Hannan \u0026amp; Liu, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The way individuals live in society is changing due to AI. AI-powered new technologies have been implemented in various economic sectors, and the educational setting is no exception. AI has been seen as essential to creating learning strategies, particularly in distant learning. Studies reveal that important issues like instructors' employability, technological training, or the moral ramifications of employing AI in education receive little attention despite the expanding use of AI in online learning (Durso \u0026amp; Arruda, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne cannot imagine an effective and robust distance learning model without using AI and machine learning applications. The problem of lack of physical interaction and face-to-face learning can be overcome to a considerable extent with various machine learning applications. The use of AI and machine learning-enabled technologies in the education system is increasing day by day (Mairal, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The last few decades have witnessed the use of technology in the education system, which has become quite helpful and lifesaving during the COVID-19 pandemic. In the difficult time of the pandemic, technology-based infrastructure equipped with AI support gave new hope to the education system. Universities and institutions kept providing quality education to their students through this platform. The role of these new technologies will be even more in a post-pandemic environment. Now, people have gone through a wonderful experience with a technology-equipped education system and its advantages, so they will be expecting more from institutions regarding educational offerings through these advanced systems (Incio Flores et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Academic institutions are adopting many AI-based intelligent tutoring and natural language processing systems through advanced AI-based algorithms. These systems are advantageous in evaluating the learning anxiety of students, which can further help them develop the confidence to learn new things. These systems are also used to address issues like the willingness to communicate by students, the knowledge acquisition capabilities of learners, and the level of classroom interaction in various classes conducted by different educators in the institute (Liang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Overall, these AI-based systems can help improve teaching and learning systems in educational institutions.\u003c/p\u003e \u003cp\u003e \u003cem\u003eRQ 1- What are the key enablers of assessing AI-based digital learning systems?\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eThe authors have dealt with ambiguous and incomplete data to answer the above RQ. The authors have also performed an extensive literature review and rigorous investigation of previous studies. Understanding these studies has helped identify the key enablers of assessing AI-based online learning platforms. Various criteria and capabilities (sub-criteria) for an AI-based online learning system have been determined by analyzing the available literature.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eRQ2- What are the uses of AI-based digital learning systems in higher education institutions?\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eTo address RQ-2, further study of the previous literature has been conducted. Previous studies in the field led us to identify key uses of AI-based online learning systems for various higher education institutions. The authors observed and presented that AI-based learning systems are useful for effective content delivery, development, research \u0026amp; innovation, and feedback management.\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eRQ-3: How can the relative and overall importance of these criteria and enablers be assessed, and what priority of focus is established for the current theme in assessing AI-based digital learning systems in higher education institutions?\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eTo acknowledge the RQ 3, the current study has performed a step-wise methodology consisting of three steps. In the first step, the identification of the enablers is done by the authors. This is done by circulating a research questionnaire to collect experts' data and then assessing the enablers based on the data quantitatively using the Pythagorean fuzzy Delphi (PF-Delphi) method. In the Second step, the selected enablers are then ranked using a combined framework of the Pythagorean fuzzy analytic hierarchy process (PF-AHP) technique and the Pythagorean Fuzzy- Delphi (PF-D) method in the third step. A sensitivity analysis is then performed to validate the results.\u003c/em\u003e \u003c/p\u003e"},{"header":"2 Literature Review","content":"\u003cp\u003eThis section contains the required literature on assessing AI-based digital learning systems in higher education frameworks, the importance of AI-based online learning systems, definitions of the deduced criteria, theoretical information of the enablers, and research contributions made by this study. It also highlights ethical issues that might be faced by education institutions while implementing AI-based online learning systems.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Appropriate article selection for assessing AI-based digital learning systems\u003c/h2\u003e \u003cp\u003eIt was imperative to review existing literature and identify the content that can help us address the research problem in the current study. For this purpose, relevant articles were searched through various key research databases like Scopus, Google Scholar, etc. This article search process aimed to select quality research papers relevant to our study. Some of the keywords that were used in this search process are \"AI-based learning systems,\" \"Online learning systems,\" \"Knowledge management system,\" \"AI in digital learning,\" and \"learning management systems in higher education,\" etc. These selected articles outlined various key aspects of AI-based learning systems used in higher education systems. When deciding which AIEd technologies to implement, HEIs must make an increasingly significant financial choice (Wu W. et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The ethical framework and standards implemented by educational authorities concerning the technological development of artificial intelligence and other related technologies have become essential in various areas of society.\u003c/p\u003e \u003cp\u003eAI benefits human societies, governments, research institutions, educational institutions, and companies only if it is implemented through proper ethical standards and practices. Without ethical standards, it may create various harms to society. The study suggested that education institutions can achieve better communication among stakeholders, delivery accuracy, and impact by using AI in online learning management systems and MOOC courses. The most profound changes are anticipated to be brought about by AIEd technology in the field of teaching and learning (Chu H C et al., 2022). Predictions of learners' learning status (including dropout and retention, student models, and academic achievement) through profiling are most frequently discussed in AI in higher education studies. The use of AI technologies in higher education has various beneficial effects, such as encouraging active learning, student engagement, and satisfaction, as well as perhaps improving learning communities and lowering feelings of isolation(Celik et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe most recent advancements in AI research and practice in higher education are essential for practitioners and researchers. Teachers must comprehend the part AI technology can play in the teaching and learning process and how to apply it to help students. Educational institution leaders must comprehend teachers' difficulties while implementing AI technology in their classrooms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 AI-based digital learning systems in Higher Education\u003c/h2\u003e \u003cp\u003eA systematic review of selected articles identified various key applications and uses of AI-based online learning platforms, which could be selected as enablers for assessing AI-based online learning systems in higher education institutions. AI can potentially help accelerate research in language studies (Liang et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Technology-based learning review models are employed to assess the role of AI in language education. Writing, reading, and vocabulary acquisition are three key application areas of AI in language education, which are delivered through various course management systems and learning content management systems.\u003c/p\u003e \u003cp\u003eAccording to the literature, many AI-based algorithms help learn anxiety, willingness to communicate, knowledge acquisition, and classroom interaction in institutions providing language education. Institutions are not using AI-based technological infrastructure to improve areas like higher-order thinking, complex problem-solving, critical thinking ability, and collaborative learning tendencies in language education. The use of AI technologies such as machine learning, deep learning, and natural language processing through learning management systems and virtual learning environments in the digitization of the educational process is increasing day by day (Salas-Pilco et al., 2022).\u003c/p\u003e \u003cp\u003eStudies revealed that the identified AI applications are very useful in addressing key issues related to higher education, such as profiling of students, probability of student dropout, dissatisfaction among students, etc., which is very useful in designing a compact system of delivering quality education to overcome such challenges of the education system in today's highly competitive environment. Bozkurt A. et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) identified two key technological areas of online education that are positively impacted by artificial intelligence. These areas are pedagogical and technological issues related to online education platforms. The study suggested five key broad research themes related to the online education environment. AI-enabled education (AIEd) is also anticipated to enable more individualized instruction and shift away from a one-size-fits-all approach to instruction and evaluation.\u003c/p\u003e \u003cp\u003eAccording to the existing literature in this area, deep learning and machine learning algorithms for online learning processes are implemented by many successful institutions for better development and delivery of content. Educational use of AI-generated data and Educational human-AI interaction is used to make the system more interactive and user-friendly. Adaptive learning and personalization of education through AI-based practices are also done through various tools of machine learning and other technological innovations, and the use of AI in higher education has become widespread among institutions throughout the world. The study also highlighted how most institutions ignore the ethical concern of AI while implementing it in their online education system (Gonzalez C. V. et al., 2021). AI-based pedagogy underlying the educational action is not reflected in the case of many institutions. Also, AI is mainly used for formative evaluation by institutions. Institutions are also using AI-based supporting systems to grade various assignments and exams conducted by institutions automatically.\u003c/p\u003e \u003cp\u003eStudies revealed that when it comes to the actual implementation of AI, institutions are not using it for educational reforms, which can be a game changer if used properly. They are using it for technological advancement and automation of the teaching administration process. Studies also emphasized the need for teacher training to implement these AI-based systems better. Chen LJ et al. (2020) highlighted many key changes and reforms brought up by AI in the education sector. They suggested that education institutions are using various AI-based systems in different forms on an extensive basis. In the initial stage, institutions started using computer-based applications of AI; gradually, they shifted to web-based AI frameworks and started using online intelligent education systems for online content delivery. They pointed out that recent advancements in AI have given education institutions opportunities to use embedded computer systems to combine AI with other technologies to offer hominoid robots and AI-based chat boxes in virtual learning environments and even metaverse technology in the future. All these applications are beneficial in administrative support, evaluation, content delivery, communication, feedback, and other areas of the education system (Mendelson E B, 2019). AI-based systems are exact and sensitive to information; at the same time, ease of use is also a vital feature of these advanced systems, making them even more applicable in various domains.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Identification of enablers for assessing AI-based digital learning systems\u003c/h2\u003e \u003cp\u003eImplications of integrating online learning and AI-enabled tools are essential for better content delivery, the importance of AI-enabled technology for processing real-time educational data, and the far-reaching impact of AI applications in online higher education (Ouyang et Al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Traditional AI technology is highly used in online teaching platforms, whereas more advanced technologies like genetic algorithms and deep learning are rarely used in the online IT-enabled infrastructure of higher education. Bearman M. et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) conducted a disclosure analysis of including text using AI to critically review the discourses of artificial intelligence in higher education to understand how to progress AI-related research and analysis. This study addressed a few confusing references related to AI as a research tool in higher education. The actual meaning and use of AI are not clear to some institutions, and they are not much aware of the far-reaching applications of AI in research and analysis of educational data through learning content management systems. In academic institutions, AI is presented as an inventible change for which all stakeholders must be prepared. This representation has a negative impact on the acceptability and adaptability of AI among teachers, students, management, and other stakeholders.\u003c/p\u003e \u003cp\u003eArtificial intelligence is a tool of competitiveness in higher education (Hannan, E. \u0026amp; Liu, S.,2021). Few studies analyzed various AI-based online learning systems used in multiple higher institutions and how these tools are helping these institutions increase competitiveness and gain competitive advantage over competitors. These studies suggested future direction for higher institutions using AI-enabled tools in their system. AI applications like learning management systems and course management systems are helping institutions in three main areas: automation of administrative operations, digitization of student learning systems, and software support to analyze student results and student support systems. The use of AI in various aspects of distance learning and human interaction is much less than in regular teaching mode (S. D. \u0026amp; Arruda, E. P., 2022). Students may need to trust the technology and think that the data supplied by a system is accurate and dependable for AIEd technologies to be wholly accepted and utilized. Educators need to use AI-based methods effectively to provide a real-time environment and to remove the barriers of contactless learning of distance learning systems. New technologies powered by AI have made distance learning much more realistic and comparative. The study also revealed that through various applications of AI, like virtual learning environments and user management systems, these institutions can offer multi-level support to instructors and learners in distance learning systems through the far-reaching impact, precision, and cognition (knowledge processing) characteristics of these systems. Training requirements and employability of teachers are two key areas of concern in the distance learning education model.\u003c/p\u003e \u003cp\u003eAlthough technology has always been crucial to higher education, it is being used more frequently than ever because of the popularity of smart devices and online courses. There are several ways artificial intelligence is being utilized to support student learning as it becomes more prevalent in higher education. The field of artificial intelligence in education (AIED) has existed for more than 60 years. We will benefit from artificial intelligence. Indeed, AI will also change higher education in the future (Flores, FAI., et al., 2022). AI has gradually contributed to the education system from time to time to reach the current level where people sitting in any part of the world can communicate and be enlightened and educated through various modes of teaching equipped with technology through advanced classroom management systems. The role of AI in education has increased drastically over the last 5\u0026ndash;6 years; during COVID, the use of AI and other related technologies has empowered the education system to a new level through offerings like massive open online courses and classroom management systems. The role of AI in the education system will be more challenging and crucial after COVID-19. Machine learning and other AI-enabled tools can ease the work of educators in the digital learning environment by saving their time and effort and facilitating better delivery of their content, but on the other hand, it can be a severe threat to their role and existence (Celik, I. et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). AI can help educators better plan their syllabus, inculcating advanced learning behavior among students developing self-efficacy in learning systems, content delivery, and student assessment. In the implementation phase also, they can use AI to take real-time feedback from students, and they can make content more presentable and application-oriented through various applications of AI like virtual learning environments and course management systems. However, implementing AI in the education system has made their job much more challenging. Now, they need to have awareness and knowledge of these new technologies and their application to use them in their teaching and learning systems effectively. Zheng, LQ et al. (2021) conducted a meta-analysis on the effectiveness of AI for academic institutions in terms of positive change in their learning outcome and learning assessment through various applications of AI, like learning content management systems, learning management systems, and course management systems. AI is widely used in academic institutions worldwide, but no research has been conducted so far on quantitative assessment of the effectiveness of AI in shaping learning achievement and learning perception in these educational institutions. AI has a powerful impact on the learning achievements of educational institutions through various uses like intelligent teaching, automotive feedback, and profiling, but it has a weak effect on the learning perception of multiple students and educators in these educational institutions.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents various criteria and their sub-criteria (identifiers), which were selected based on a detailed analysis of key literature available in the study area. As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e four key criteria- Uses in online teaching, uses in R \u0026amp; D of learning platform, uses in review \u0026amp; feedback of learning platform, and characteristics of AI-based learning platforms are employed in previous studies to assess AI-based online learning platforms.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCriteria for assessing alternative AI-based digital learning systems\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSr. No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSub criteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAbbreviations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eContributors\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUses in Online Teaching\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntelligent Tutoring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChen LJ et al. (2020), Flores, FAI. et al (2022), Zheng, LQ et. al (2021)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAutomotive Grading\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGonzalez C. V. at al (2021), Chen LJ et al (2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFormative Assessment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUFA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGonzalez C. V. at al (2021), Chen LJ et al (2020), Zheng,\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUses in R \u0026amp; D of learning platform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResearch \u0026amp; Analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUR\u0026amp;A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChu H. C. et al. (2022), Bearman M. et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePedagogy Development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUPD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBozkurt A. et al (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Celik, I. et al (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContent Development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUCD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBozkurt A. et al (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUses in Review and Feedback of Learning Platform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProfiling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSalas-Pilco et al (2022), Chu H. C. et al (2022), Zheng, LQ et. al (2021)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAutomotive Feedback Management\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUAFD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCelik, I. et al (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), LQ et. al (2021)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePolicy Implementation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eChu H. C. et al. (2022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eCharacteristics of AI-based learning platform\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLearning Behavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCLB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCelik, I. et al (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWu W. et al (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Celik, I. et al (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMendelson E B (2019)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrecision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMendelson E B (2019), Durso, S. D. \u0026amp; Arruda, E. P. (2022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCognition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDurso, S. D. \u0026amp; Arruda, E. P. (2022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImpact\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDurso, S. D. \u0026amp; Arruda, E. P. (2022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCommunication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCCO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBozkurt A. et al (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Wu W. et al (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Chen LJ et al (2020), Flores, FAI. et al (2022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-efficacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCelik, I. et al (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEase of Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCEU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMendelson E B (2019)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eSource\u003c/b\u003e: Author's computation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThese criteria are further subdivided into various capabilities to analyze the detailed impact of each criterion in assessing AI-based online learning systems.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of AI-based Digital Learning Systems used in higher education institutions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSN.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAI-Based Online Learning Systems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAbbreviations\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eContributors\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLearning Management System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSalas-Pilco et al (2022), Wu W. et al (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Hannan, E. \u0026amp; Liu, SG. (2021), Zheng, LQ et al. (2021)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLearning Content Management System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLCMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBearman, M. et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Chu H. C. et al. (2022), Zheng, LQ et al. (2021), Liang, JC et al. (2021)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClassroom Management System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFlores, FAI. et al (2022), Flores, FAI. et al. (2022),\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVirtual Learning Environment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVLE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSalas-Pilco et al (2022), Chen LJ et al (2020), Durso, S. D. \u0026amp; Arruda, E. P. (2022), Celik, I. et al (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCourse Management System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCOMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHannan, E. \u0026amp; Liu, SG. (2021), Celik, I. et al (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Zheng, LQ et. al (2021), Liang, JC et. al. (2021)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUser Management System\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUMS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHannan, E. \u0026amp; Liu, SG. (2021), Durso, S. D. \u0026amp; Arruda, E. P. (2022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIntelligent Education System/ Supporting Systems\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIES/SS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChen LJ et al. (2020)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMassive Open Online Courses\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMOOC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWu W. et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), Flores, FAI. et al (2022)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cb\u003eSource\u003c/b\u003e: Author's computation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows various AI-based digital learning systems used by multiple educational institutions to achieve different objectives. There are a total of 8 types that various education institutions commonly use in their online learning systems. Each of these systems is useful in different ways.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Articulation of theoretical framework\u003c/h2\u003e \u003cp\u003eA review of the literature conducted in this study also targeted establishing theoretical linkage for addressing key research questions raised in this study. Through a thorough examination of existing literature, theoretical linkages were established for solving the research problems of the current study.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCustomer satisfaction index model (ACSI)\u003c/b\u003e is a cause-effect model that says that satisfaction is caused by each factor involved as an input. This theory is expanded in the education sector to study the usefulness and effectiveness of various AI-based online learning platforms based on various characteristics like ease of use, accuracy, precision, cognition, and self-efficacy of these AI-based systems (Matsatsinis, NF et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). In an e-learning environment, this theory links these characteristics of AI-based digital learning systems used in higher education institutions as key driving forces that affect these systems' usefulness and accountability. \u003cb\u003eLearning condition theory\u003c/b\u003e believes that overall learning is affected by internal and external conditions, which consist of various factors. Internal condition refers to factors like self-efficacy, learning behaviors, and cognition. External factors include communication, impact, and sensitivity of AI-based learning. In an e-learning environment, online teaching methods and tools/ software used in an e-learning platform are key external factors affecting profiling, prediction, intelligent tutoring, and formative assessment in AI-based online learning systems (Hwang, G.J. et al., 2022). \u003cb\u003eSocial cognitive theory\u003c/b\u003e believes that knowledge creation for the student in online learning is a social context because, in this process, students interact with other social elements, engage in various activities, and receive inputs from multiple people. So overall impact of any technological tool for assisting the process of knowledge creation should be measured in terms of the interaction of these entities and the usefulness of that technology to various stakeholders who interact in the process of knowledge creation (Swiecki, Z., et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The theoretical framework of this study is inspired by social cognitive theory, Fornell's customer satisfaction index model (ACSI), and learning condition theory. This study proposes a hybrid theoretical model combining these three key theories. Various criteria and sub-criteria proposed through the theoretical model are extracted through the interaction of these theories, which are very crucial for the assessment of the use of AI-based digital learning platforms in higher education institutions, which are designed to cope with the challenges of delivering quality education through digital mode in normal time as well as pandemic period.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Methods","content":"\u003cp\u003eThis section discusses how enablers were selected using the specified criteria and then ranks them according to their calculated weightage. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the research process flow that was used in this study.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Pythagorean Fuzzy Sets\u003c/h2\u003e \u003cp\u003eReal-world input data for multi-criteria decision-making (MCDM) are insufficient and ambiguous. Intuitionistic fuzzy sets, which can be expressed as membership functions, non-membership functions, and hesitation degrees, can manage this ambiguity. When membership and non-membership have degrees greater than 1, intuitionistic fuzzy sets cannot consider uncertainty. However, the issue is solved by Pythagorean Fuzzy Sets (PFS), an expansion of intuitionistic fuzzy sets. Introducing Pythagorean Fuzzy Sets (PFS), Yager (2013). Like fuzzy sets, these sets handle selection ambiguity and permit flexible reasoning following human norms. An object called a Pythagorean Fuzzy Set (PFS) P is defined as:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\text{P}=\\{\u0026lt;x, {{\\mu }}_{\\text{p}}\\left(\\text{x}\\right), {{\\upvartheta }}_{\\text{p} }\u0026gt;|x\\in X\\}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$${{\\mu }}_{\\text{p}} \\left(\\text{x}\\right) \\in \\left[\\text{0,1}\\right] {{\\upvartheta }}_{\\text{p}} \\left(\\text{x}\\right)\\in [0, 1]$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$0\\le {{\\mu }}^{2}+ {{\\upvartheta }}^{2} \\le 1$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ePythagorean Fuzzy Sets can be used to calculate the Degree of Determinacy (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\pi }\\)\u003c/span\u003e\u003c/span\u003e) since they are driven by a membership function (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mu }\\)\u003c/span\u003e\u003c/span\u003e) and a non-membership function (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\upvartheta }\\)\u003c/span\u003e\u003c/span\u003e).\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$${\\pi }= \\sqrt{(1}-{{\\mu }}^{2}- {{\\upvartheta }}^{2})$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eOverall, membership (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\mu }\\)\u003c/span\u003e\u003c/span\u003e) and non-membership degrees (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\upvartheta }\\)\u003c/span\u003e\u003c/span\u003e) of Pythagorean fuzzy sets can be more than 1, but not their squares. The intuitionistic membership grades are below the line x\u0026thinsp;+\u0026thinsp;y\u0026thinsp;\u0026le;\u0026thinsp;1, and the Pythagorean membership grades are with x 2\u0026thinsp;+\u0026thinsp;y 2\u0026thinsp;\u0026le;\u0026thinsp;1 for all the points (x, y) with both an intuitionistic and a Pythagorean membership grade. The Pythagorean membership grade offers greater flexibility to solve the worry of uncertainties and mistakes in expert input data, as shown by a comparison between the Pythagorean membership grade and intuitionistic membership grade in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Pythagorean Fuzzy- Delphi (PF-D)\u003c/h2\u003e \u003cp\u003ePythagorean Fuzzy-Delphi, which combines the classic Delphi method and Fuzzy sets, provides decisions based on human evaluation (Zadeh et al. 1996). The analysis quality has been enhanced using Pythagorean fuzzy sets (Sindhwani et al., 2022). The subsequent steps were performed to apply the method:\u003c/p\u003e \u003cp\u003eStep I: Based on the current literature research, the identified criterion and the set of enablers are given in tabular format for expert evaluation.\u003c/p\u003e \u003cp\u003eStep II: Expert opinions were expressed in linguistic form and converted to Pythagorean fuzzy numbers using the scale presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. According to A. Kumar et al. (2018), Eq.\u0026nbsp;(1) indicates the evaluation score (Si j) provided by j experts for i enablers:\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e\n$${\\text{S}}_{\\text{i}\\text{j}}=\\left( {{\\mu }}_{\\text{i}\\text{j}, }{{\\upvartheta }}_{\\text{i}\\text{j}}\\right) \\left(1\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eStep III: The row-wise sets are subjected to a union operation to produce the combined structure required by Eq.\u0026nbsp;(2) (Abdullah \u0026amp; Goh, 2019):\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e\n$${\\alpha }=\\left(\\text{max}{{\\mu }}_{\\text{i}\\text{j} },\\text{min}{{\\upvartheta }}_{\\text{i}\\text{j}}\\right) \\left( {{\\mu }}_{\\text{i}}^{{\\prime }}, {{\\upvartheta }}_{\\text{i}}^{{\\prime }}\\right) \\left(2\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eStep IV: Calculate the degree of hesitancy following Eq.\u0026nbsp;(3):\u003cdiv id=\"Equg\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equg\" name=\"EquationSource\"\u003e\n$${{\\pi }}_{\\text{i}}^{{\\prime }}=1- {{\\mu }}_{\\text{i}}^{{\\prime }2}- {{\\upvartheta }}_{\\text{i}}^{{\\prime }2} \\left(3\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eStep V: Finding a crisp value for each enabler using Eq.\u0026nbsp;(4):\u003cdiv id=\"Equh\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equh\" name=\"EquationSource\"\u003e\n$${\\text{d}}_{\\text{f} }\\left({\\alpha }\\right)= \\frac{1+ {{\\mu }}_{\\text{i}}^{2} -{{\\upvartheta }}_{\\text{i}}^{2}- {{\\pi }}_{\\text{i}}^{{\\prime }2} }{2} \\left(4\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRating of capability and criteria using Linguistic Terms (Liu et al., 2021)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLinguistic Term\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePFN\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerfectly High (PH.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e(0.950, 0.200)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery High (VH.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e(0.850, 0.350)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh (H)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e(0.700, 0.400)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium High (MH.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e(0.650, 0.450)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage (A)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e(0.500, 0.550)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium Low (ML)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e(0.400, 0.650)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow (L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e(0.350, 0.750)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery Low (VL.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e(0.250, 0.850)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery Very Low (VVL)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e(0.200, 0.950)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cb\u003eSource\u003c/b\u003e: Author's computation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Pythagorean Fuzzy -Analytic Hierarchy Process (PF-AHP)\u003c/h2\u003e \u003cp\u003eIn contrast to other knowledge-based approaches like ANP, TOPSIS, and ELECTRE, AHP provides a superior way of resolving MCDM issues and communicating efficient outcomes (Keshavarz Ghorabaee et al., 2017). The PFN-AHP technique to deal with ambiguity and imprecision assigns a relative relevance score and integrates with conventional AHP.\u003c/p\u003e \u003cp\u003eTo include PF-AHP in the study, the steps below were taken:\u003c/p\u003e \u003cp\u003eStep I: Ilbahar et al. (2018) created the pair-wise comparison matrix \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{X}}_{\\text{i}\\text{j}}=({\\text{X}}_{\\text{i}\\text{j}}{)}_{\\text{m}\\times \\text{m}}\\)\u003c/span\u003e\u003c/span\u003e for each criterion using the linguistic phrases shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e as a basis.\u003c/p\u003e \u003cp\u003eStep II: Utilising the membership and non-membership functions from equations (5) and (6), compute the difference matrix:\u003cdiv id=\"Equi\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equi\" name=\"EquationSource\"\u003e\n$$Lower {d}_{ij}= {\\mu }_{ij}^{2}- {\\vartheta }_{ij}^{2} \\left(5\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equj\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equj\" name=\"EquationSource\"\u003e\n$$Upper {d}_{ij}= {\\mu }_{ij}^{2}- {\\vartheta }_{ij}^{2} \\left(6\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eStep III: Utilizing equations (7) and (8), compute the interval multiplication matrix:\u003cdiv id=\"Equk\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equk\" name=\"EquationSource\"\u003e\n$$\\text{L}\\text{o}\\text{w}\\text{e}\\text{r} {\\text{S}}_{\\text{i}\\text{j}}=\\sqrt{ {1000}^{\\text{d}} } \\left(7\\right)$$\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equl\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equl\" name=\"EquationSource\"\u003e\n$$\\text{U}\\text{p}\\text{p}\\text{e}\\text{r} {\\text{S}}_{\\text{i}\\text{j}}= \\sqrt{{1000}^{\\text{d}} } \\left(8\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eStep IV: Following Eq.\u0026nbsp;(9), the determinacy value (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\tau }\\)\u003c/span\u003e\u003c/span\u003e) of the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{X}}_{\\text{i}\\text{j}}\\)\u003c/span\u003e\u003c/span\u003e is calculated as follows:\u003cdiv id=\"Equm\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equm\" name=\"EquationSource\"\u003e\n$${\\tau }=1-\\left({\\text{U}\\text{p}\\text{p}\\text{e}\\text{r} {\\mu }}_{\\text{i}\\text{j}}^{2}- \\text{L}\\text{o}\\text{w}\\text{e}\\text{r} {{\\mu }}_{\\text{i}\\text{j}}^{2}\\right)- \\left({\\text{U}\\text{p}\\text{p}\\text{e}\\text{r} {\\upvartheta }}_{\\text{i}\\text{j}}^{2}- \\text{L}\\text{o}\\text{w}\\text{e}\\text{r} {{\\upvartheta }}_{\\text{i}\\text{j}}^{2}\\right) \\left(9\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eStep V: Determinacy degrees are multiplied with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{S}=({\\text{S}}_{\\text{i}\\text{j}}{)}_{\\text{m}\\times \\text{m}}\\)\u003c/span\u003e\u003c/span\u003e to get a matrix of weights, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{T}={(\\text{t}}_{\\text{i}\\text{j}}{)}_{\\text{m}\\times \\text{m}}\\)\u003c/span\u003e\u003c/span\u003e before normalization using Eq.\u0026nbsp;(10):\u003cdiv id=\"Equn\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equn\" name=\"EquationSource\"\u003e\n$${\\text{t}}_{\\text{i}\\text{j}}=\\left(\\frac{\\text{L}\\text{o}\\text{w}\\text{e}\\text{r} {\\text{S}}_{\\text{i}\\text{j}}+ \\text{U}\\text{p}\\text{p}\\text{e}\\text{r} {\\text{S}}_{\\text{i}\\text{j}}}{2}\\right) {{\\tau }}_{\\text{i}\\text{j}} \\left(10\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eStep VI: Normalized priority weight \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{w}}_{\\text{i}}\\)\u003c/span\u003e\u003c/span\u003e using Eq.\u0026nbsp;(11):\u003cdiv id=\"Equo\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equo\" name=\"EquationSource\"\u003e\n$${\\text{w}}_{\\text{i}}= \\frac{{\\sum }_{\\text{j}=1}^{\\text{m}}{\\text{t}}_{\\text{i}\\text{j}}}{{\\sum }_{\\text{i}=1}^{\\text{m}}{\\sum }_{\\text{j}=1}^{\\text{m}}{\\text{t}}_{\\text{i}\\text{j}}} \\left(11\\right)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eScale of relative importance for AHP (Ilbahar et al. 2018)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLinguistic Term\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003ePythagorean fuzzy numbers (PFN)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026micro; L\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026micro; U\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eνL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eνU\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCertainly, Low Importance (CLI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery Low Importance (VLI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow Importance (LI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBelow Average Importance (BAI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage Importance (AAI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbove Average Importance (AAI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh Importance (HI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery High Importance (VHI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCertainly, High Importance (CHI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExactly Equal (EE.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u003cb\u003eSource\u003c/b\u003e: Author's computation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Results","content":"\u003cp\u003eThis work aims to offer a set of evaluation criteria for digital learning systems powered by AI. Relevant criteria needed to be sorted into the appropriate categories to understand future research and development in this field. Additionally, it's crucial to prioritize these facilitators and carry out a quantitative analysis. Given the state of technology today, society and corporations can conduct research and development (R\u0026amp;D) and think about novel ideas. Therefore, expert input from academia, business, and research experts is essential to offer professional judgments and vital insights. Using the snowball sampling method, a panel of six experts was chosen to assess the acceptance of the suggested facilitators. The Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e lists the specifics of the selected specialists, their specialties, and the lengths of time they have worked in each subject. The experts' chosen biographical information, including their industry of specialization, educational background, work history, and critical roles, is displayed in the Table below.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExperts Profile\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperts\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndustry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRole\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eQualification\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExperience (Years)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExpertise\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEdTech\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eManager\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMBA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eEmerging technology adoption for enriching learning experience.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigher Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProfessor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePh.D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTeaching and research interest revolves around ICT tools and their adoption for better learning experience.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigher Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProfessor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePh.D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWorks on developing sustainable systems with machine-human connection. Worked on many projects based on sustainability, technology, and education.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEdTech\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAssistant Manager\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM Tech\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eExpert in demonstrating the use of AI, ML, and other emerging technologies in simplifying the learning process in education.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eManagement Consulting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResearch Analyst\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMBA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAssociated with a consulting firm and working on the implication of disruptive innovation in the educational space.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eE6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAcademic Publishing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eManager\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMBA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e16+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eProficient in interactive e-content development for a leading publishing group.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eSource\u003c/b\u003e: Author's computation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Stage I Finalisation of criterion and enablers\u003c/h2\u003e \u003cp\u003eFour criteria and eighteen capabilities for an AI-based digital learning system have been identified based on an analysis of the available literature. The ambiguity of the option was handled with Pythagorean fuzzy-delphi. A questionnaire about the overall acceptability of ability was supplied to the expert panel. The response was recorded in language terms and subsequently converted to PFNs (Pythagorean fuzzy numbers), as shown in Table\u0026nbsp;3.1. To discover the crisp values specified in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e in further detail, the obtained PFNs are also defuzzified. Data pertaining to language was processed using the PF-Delphi algorithm. Based on the literature, a threshold value of 0.6 was used to determine if capabilities were accepted or rejected (Shen et al., 2019).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePythagorean fuzzy weights and de-fuzzified values\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCapability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026micro;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eϑ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eπ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ed\u003csub\u003ef\u003c/sub\u003e (α)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUses in Online Teaching (UOT)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUOT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntelligent Tutoring\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUOT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAutomotive Grading\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUOT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFormative Assessment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUses in R \u0026amp; D of learning platform (URD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eURD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResearch \u0026amp; Analysis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eURD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePedagogy Development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eURD3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eContent Development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUses in Review and Feedback of Learning Platform (URF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eURF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProfiling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eURF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAutomotive Feedback\u003c/p\u003e \u003cp\u003eManagement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eURF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePolicy Implementation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"8\" rowspan=\"9\"\u003e \u003cp\u003eCharacteristics of AI-based learning platform (CAI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAI1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLearning Behaviour\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.147\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAccuracy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAI3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.147\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAI4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrecision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.147\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAI5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCognition\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.788\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAI6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eImpact\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.930\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAI7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCommunication\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAI8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-efficacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCAI9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEase of Use\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.147\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eSource\u003c/b\u003e: Author's computation\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eNote\u003c/b\u003e: Capability with a threshold value greater than 0.6 was retained for the next phase of analysis.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Stage II Relative weight calculation and ranking\u003c/h2\u003e \u003cp\u003eThe creation of a hierarchy model was the main objective in assembling the expert panel. Three stages in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e depict the proposed hierarchy concept. Level I: AI-based learning (the purpose); Level II: Categorising the criteria; and Level III: Evaluating the capabilities of the major criteria.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe aforementioned survey data was processed using the PF-AHP algorithm. Based on the specified procedures in Section 3, the difference matrix, interval multiplicative matrix, determinacy value matrix, and normalized priority weight were generated and displayed in Tables\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e and \u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePair-wise Comparison Decision Matrix for Criteria\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"17\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eUOT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e \u003cp\u003eURD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c13\" namest=\"c10\"\u003e \u003cp\u003eURF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c17\" namest=\"c14\"\u003e \u003cp\u003eCAI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026micro; L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026micro; U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eνL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eνU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026micro; L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026micro; U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eνL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eνU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u0026micro; L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u0026micro; U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eνL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eνU\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u0026micro; L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e\u0026micro; U\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003eνL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003eνU\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUOT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eURD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eURF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c16\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c17\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"17\"\u003e\u003cb\u003eSource\u003c/b\u003e: Author's computation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eWeights before normalization and normalized weight\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUOT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eURD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eURF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCAI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNormalized Weight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUOT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eURD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eURF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.77\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cb\u003eSource\u003c/b\u003e: Author's computation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSimilar methods were employed to determine the capability's priority ranking, but this calculation is not displayed due to paper limitations. The results of the PF-AHP method led to the determination of the local and global capability weights. In addition, Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e displays each capability's overall ranking as determined by the perspectives of eight experts. The order in which the capability's priority rating was determined is as follows: CAI\u0026thinsp;\u0026gt;\u0026thinsp;URD\u0026thinsp;\u0026gt;\u0026thinsp;URF\u0026thinsp;\u0026gt;\u0026thinsp;UOT.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eImportance weight of capabilities\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCriteria weight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCapability Code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCapability local weight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGlobal weight\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGlobal rank\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eUOT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUOT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUOT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUOT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eURD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eURD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.074\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eURD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eURD3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eURF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eURF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eURF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eURF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eCAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCAI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCAI5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCAI6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCAI7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCAI8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cb\u003eSource\u003c/b\u003e: Author's computation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Stage III Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eSensitivity analysis is one of the most efficient ways to evaluate a model's reliability and applicability (Abdullah and Goh, 2019). This method is used in the third step to validate the PF-AHP results and test the sensitivity of the used model under constant conditions. Any modifications to the results must be incorporated into the updated framework output (Kumar et al., 2019). The challenges with the highest and second-highest weights were taken into account to apply the technique since it was thought that even small changes in their values would result in significant differences across the rankings of the challenges. Hence, the Characteristics of AI-based learning platform (CAI) was considered. Their weight values varied proportionally from 0.9 times to 0.1 times. For example, the weight of CAI is 0.327, which varies from 0.9*0.327, 0.8*0.327, to 0.1*0.327. The resulting variations in the weights of other challenges are provided in Table\u0026nbsp;\u003cspan refid=\"Tab10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab10\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 10\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValue of main criteria with sensitivity on CAI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUOT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.218\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.244\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eURD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.301\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.325\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.363\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.379\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eURF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.292\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.316\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cb\u003eSource\u003c/b\u003e: Author's computation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCorresponding to the variations in CAI values, changes in the capability ranks were also observed, and CAI8 and CAI7 showed major variations. Table\u0026nbsp;\u003cspan refid=\"Tab11\" class=\"InternalRef\"\u003e11\u003c/span\u003e shows the variations in capability ranks, and the results are graphically represented in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab11\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 11\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariation in capability with sensitivity on CAI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCapability\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.9\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUOT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUOT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUOT3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eURD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eURD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eURD3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eURF1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eURF2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eURF3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAI2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAI5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAI6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAI7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAI8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003cb\u003eSource\u003c/b\u003e: Author's computation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5 Discussions and Research Propositions","content":"\u003cp\u003eThis study proposes various theoretical and practical implications that may be adopted by higher education institutions at various levels for enhancing the structure and delivery model of online learning platforms.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eProposition 1\u003c/strong\u003e \u003cp\u003e \u003cem\u003eOnline learning systems must be customized by implementing artificial intelligence to enhance the characteristics of digital learning platforms\u003c/em\u003e.\u003c/p\u003e \u003c/p\u003e \u003cp\u003eAI-based digital learning platforms are reshaping the learning experience in higher education institutions. Adopting AI-based technologies will enhance the various characteristics of digital learning systems (Celik I. et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Better features would mean more acceptability and reach of these digital learning platforms among students and educators. AI-based learning systems will offer customized learning behavior, higher accuracy, and sensitivity to digital learning systems. The content and process of digital learning will become more impactful, precise, and cognitive. Overall, communication among various users of digital learning platforms will also improve through multiple channels of artificial intelligence (Bozkurt A. et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eProposition 2\u003c/strong\u003e \u003cp\u003e \u003cem\u003eOnline teaching in higher education institutions can be enhanced using AI-based digital learning systems.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe penetration of AI has changed the traditional model of teaching. Today's teaching has become more output-oriented and informative (Colchester K. et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). AI can scientifically integrate various teaching technologies to make the teaching process systematic and effective. Digital learning systems will integrate features like intelligent tutoring, automotive grading, and formative assessment to enhance higher education institutions' overall teaching and learning ecosystem (Ben Ammar M. et al.,2010). These add-ons will drastically improve the effectiveness and attractiveness of the teaching process. Educators can achieve customized delivery, resource acquisition, individual learning, and talent training through various advanced tools of AI in digital learning environments (Zheng LQ et al.,2021).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eProposition 3\u003c/strong\u003e \u003cp\u003e \u003cem\u003eResearch and development outcomes in higher education institutions can be improved using AI-based digital learning systems.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eAI has brought advancement in research \u0026amp; development carried out in different areas through many supportive technologies \u0026amp; software designed for conducting qualitative \u0026amp; quantitative research. Remote learning platforms enabled by AI have colossal potential to democratize research \u0026amp; development in higher education institutions by improving the accessibility of research data (Zawacki-Richter et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Researchers worldwide can access quality research data from various research databases and secondary data sources and convert data into multidimensional forms using AI-enabled software and tools. It can also help researchers identify the potential threat of plagiarism in research and act as a warning signal. AI offers many simulation software, data modifiers, analysis tools, and modeling software, which can be very useful to researchers associated with higher education institutions (Franzoni V. et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). AI algorithms can also help researchers identify knowledge \u0026amp; research gaps to provide targeted remedial resources in different research fields. Lastly, AI can help researchers form global ties for multidisciplinary and multicontinental research. Through AI-empowered digital learning platforms, content and pedagogy development will become more effective and fruitful (Bearman M et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eProposition 4\u003c/strong\u003e \u003cp\u003e \u003cem\u003eReview and feedback systems in higher education institutions can become more effective and compact by using AI techniques in digital learning systems.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eReview \u0026amp; feedback is an essential supportive pillars of quality education. It helps learners in achieving learning goals and improving self-regulated skills. The role of review \u0026amp; feedback becomes even more critical in a digital learning environment. Physically and geographically distant instructors and learners can understand each other, communicate with each other, and customize interaction based on review \u0026amp; feedback (Cavalcanti, A. et al.,2021). Through AI-enabled systems, the process of generating, analyzing, and communicating feedback can become time-efficient and effective. AI can even help the instructor collaborate and analyze large amounts of feedback received from multiple sources. Higher education institutions can use AI-enabled online feedback systems to improve student performance and satisfaction (Blikstein et al.,2014).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eProposition 5\u003c/strong\u003e \u003cp\u003e \u003cem\u003eAdopting the proposed enablers in digital learning platforms will ensure better performance and acceptability of digital learning platforms in higher education institutions.\u003c/em\u003e \u003c/p\u003e \u003c/p\u003e \u003cp\u003eThe development of AI is becoming essential for economic sustainability. The education sector is also inseparable from the support of AI in today's digital education environment. Digital learning platforms enabled through AI can be very helpful to various stakeholders of educational institutions (Chu H. et al., 2022). With the developmental role of AI in the online learning environment, education delivery in higher education institutions is developing more and more in the direction of intelligent and informative learning. In a global education environment, AI-based digital learning platforms could be game changers with far-reaching impacts on the outcome of education delivery (Arruda, E. P.,2022). So higher education institutions must integrate AI in their digital learning system to cultivate smart learning, integrate rich and techno-oriented teaching skills, enrich research \u0026amp; development experience, and establish strong feedback \u0026amp; support systems for continuous improvement of the learning system (Bozkurt, A. et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). AI must be integrated into the education delivery model of higher education institutions to enhance the reach, connectivity, and productivity of digital learning systems (Zawacki-Richter O. et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eEducation is one of the industries that offers a vast scope for various applications of AI. In recent years, rapid development and transformation have been witnessed in the education sector through AI. In the digital learning environment, higher education institutions may achieve personalized learning experiences using AI. AI offers many exciting developments for improving higher education worldwide through AI-enabled digital learning systems. AI can significantly enhance the learning process by providing greater precision, technology-empowered teaching, multi-fold uses, and a more accurate review \u0026amp; feedback system. Thus, AI-enabled digital learning systems can help students and instructors develop and implement different learning pathways or provide them with real-time feedback to strengthen the teaching \u0026amp; learning process in higher education institutions.\u003c/p\u003e \u003cp\u003eThe penetration of AI in digital learning systems has transformed the traditional way of teaching methods and models. Higher education institutions are cultivating general interest, sustainable development, and learning outcomes of students by inculcating AI in their learning model (Chen et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Various AI tools can meet individual users' expectations and push the optimal digital learning resources, digital learning path, and research \u0026amp; innovation in higher education institutions (Wu et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). AI is a multidimensional collaborator in higher education that has the power to collaborate with multiple domains, geographically diverse users, and globally scattered information through AI-powered digital learning platforms (Zheng et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Coming-of-age AI and its applications have brought many revolutionary reforms in the higher education system by updating many out-of-date concepts, methods, processes, training systems, feedback systems, and teaching \u0026amp; learning processes (Hannan \u0026amp; Liu, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). AI has given new wings to digital learning in higher education to bring about lifelong education, borderless learning, smart campus, and smart learning in higher education institutions (Arruda, E. P., 2022).\u003c/p\u003e \u003cp\u003eIn the present study, authors have covered the majority of execution points addressed by research questions and research objectives by reviewing the most relevant literature and identifying the most impactful criteria and enablers to assess the impact of AI-enabled digital learning platforms in higher education institutions. MCDM through PFS, PF-Delphi, and PF-AHP methods were employed for producing effective results. However, like any other method, these methods have some limitations which may be explored by other researchers. The unbiased opinion of an expert panelist is a must for producing explicit results; thus, any form of bias in judging must be omitted. Despite some limitations, the study has produced useful results in AI-based digital learning systems and created scope for future research in the same area. The output ranking of enablers provides an essential priority list for assessing AI-based digital learning systems, which can be referred by higher education institutions, software developers, and policymakers for designing AI-enabled digital learning systems. Thus, this study gives a clear idea about assessing AI-based digital learning platforms in higher education institutions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e The authors did not receive any financial support for this research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest:\u003c/strong\u003e The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHuman participants and/or animals:\u003c/strong\u003e The authors ensure that no animal participation is involved in this research. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed conssent:\u003c/strong\u003e Informed consent was obtained from all individual participants included in the study.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBearman, M., Ryan, J., \u0026amp; Ajjawi, R. (2022). Discourses of artificial intelligence in higher education: a critical literature review. \u003cem\u003eHigher Education\u003c/em\u003e. https://doi.org/10.1007/s10734-022-00937-2.\u003c/li\u003e\n\u003cli\u003eBen Ammar, M., Neji, M., Alimi, A. M., and Gouarderes, G. (2010). The affective tutoring system. \u003cem\u003eExp. Syst\u003c/em\u003e. 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Interactive Learning Environments, 1\u0026ndash;15. https://doi.org/10.1080/10494820.2021.2015693\u003c/em\u003e.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"international-journal-of-system-assurance-engineering-and-management","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"ijsa","sideBox":"Learn more about [International Journal of System Assurance Engineering and Management](http://link.springer.com/journal/13198)","snPcode":"13198","submissionUrl":"https://www.editorialmanager.com/ijsa/default2.aspx","title":"International Journal of System Assurance Engineering and Management","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Teaching/learning strategies, Data science applications in education, Evaluation methodologies","lastPublishedDoi":"10.21203/rs.3.rs-3828524/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3828524/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe devastating effects of the 2020 worldwide COVID-19 virus epidemic prompted widespread lockdowns and restrictions, which will continue to be felt for decades. The repercussions of the pandemic have been most noticeable among educators and their students, which boosts the effectiveness of various AI-based learning systems in the education system. This study examines the AI-based digital learning platforms in higher education institutions based on various characteristics and uses of these systems. Several significant aspects of AI-based digital learning systems were obtained from the available literature, and significant articles were selected to properly examine various characteristics and functions of AI-based digital learning platforms used by multiple higher education institutions. The analytical hierarchy process (AHP) is employed to rank multiple AI-based learning systems based on key factors and their sub-factors. 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