Cognitive Metamorphosis in Higher Education: AI-LTs Nexus of DeepSeek, GPT, and the Future of Scholarly Engagement

Cognitive Metamorphosis in Higher Education: AI-LTs Nexus of DeepSeek, GPT, and the Future of Scholarly Engagement | 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 Systematic Review Cognitive Metamorphosis in Higher Education: AI-LTs Nexus of DeepSeek, GPT, and the Future of Scholarly Engagement Anwar Ali Sathio, Muhammad Malook Rind, Mahboob Ali Naper, Ghulam Ahmed, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6460706/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Artificial Intelligence based Learning Tools (AI-LTs) are rapidly transforming higher education by enhancing teaching, learning, and administration. This study systematically reviews peer-reviewed literature from 2020 to 2024 to explore the roles, benefits, and challenges of AI-LTs such as DeepSeek, ChatGPT, Meta AI, and Gemini. Using a qualitative methodology, we identified and analyzed studies from Scopus and Web of Science databases, applying rigorous selection and data extraction criteria. Findings reveal that AI-LTs significantly improve personalized learning, student engagement, and administrative efficiency but also raise ethical concerns including algorithmic bias and data privacy risks. The study emphasizes the need for responsible AI integration through faculty training, transparent algorithms, and human-AI collaboration. Future research should focus on primary empirical studies to validate AI-LTs impacts in diverse educational contexts. AI-LTs Higher Education Teaching Learning Adaptive Platforms Ethical Challenges Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction The role of AI tools has revolutionized traditional ways of learning, teaching, and research in higher educational institutions globally. Since AI technologies have evolved and influenced global applications, teaching and learning processes in higher education are becoming increasingly creative, as shown in Table 1. There is a significant knowledge gap in how various stakeholders in the education field, such as faculty, administrators, and students, perceive the applications of GPT-based AI tools and their potential future role globally. The integration of AI tools has transformed traditional approaches to learning, teaching, and academic research in higher education globally. With the evolution of AI technologies such as GPT-based systems, educational practices have become increasingly innovative, adaptive, and learner-centered. As shown in Table 1, the role of AI-based learning tools (AI-LTs) is expanding across diverse domains. However, there remains a significant knowledge gap in understanding how various stakeholders, faculty, administrators, and students—perceive and engage with these tools, particularly in terms of their long-term academic and institutional implications. This study specifically aims to: (1) examine the impact of AI-based tutoring systems and adaptive learning platforms on student outcomes, (2) explore faculty adoption and pedagogical enhancements via AI-LTs, and (3) critically assess the ethical and societal dimensions of AI integration. To provide contextual relevance, Table 1 presents a comprehensive comparative analysis of key AI-LTs currently shaping higher education. Table 1 : Comprehensive Comparative Analysis of AI-Based Learning Tools in Higher Education Learning Metric DeepSeek ChatGPT Meta AI Gemini AI Other AI-Based Learning Tools Key Findings & Scholarly References Studies Personalized Learning High adaptability in domain-specific queries and research-based interactions. Strong contextual understanding and personalized tutoring features. Adaptive content generation for various disciplines. Multimodal learning support with text, images, and coding. Socratic AI, Squirrel AI, Querium, Knewton, DreamBox Learning AI tools provide 30-40% better personalized learning experiences [73,124] Student Engagement Interactive and dynamic response generation. High conversational engagement with real-time feedback. Integrated AI-driven gamification for interactive learning. Enhanced engagement with multimedia capabilities. Nearpod, Kahoot, Edpuzzle, Flipgrid, Wooclap AI-driven tools increase student engagement by 25-35% [16-17] Adaptive Learning Context-aware learning and real-time data-driven personalization. Adjusts responses based on student progress and comprehension. AI-powered intelligent tutors for customized learning paths. Offers real-time assistance with multi-faceted reasoning. Smart Sparrow, Carnegie Learning, ALEKS, Knowji AI-based adaptive learning improves retention rates by 20% [26] Critical Thinking Development Supports deep research-based inquiries. Encourages reasoning-based discussions. Generates creative learning insights using cognitive AI. Provides logical reasoning with complex analytical capabilities. Raven AI, IBM Watson Education, Thinkster Math AI integration in education fosters 18% improvement in critical thinking [107-108] Writing and Research Assistance Strong in AI-driven content generation for research. Offers structured writing support with citations. Summarizes and synthesizes academic literature. Provides multimodal citation-based writing assistance. Grammarly, QuillBot, Hemingway Editor, Scite, Zotero, Mendeley AI tools improve research efficiency by 30% [76] Language & Communication Skills Supports multilingual understanding and translation. Excellent conversational AI with natural language processing. AI-powered real-time voice synthesis. Advanced linguistic models with nuanced comprehension. Duolingo, Babbel, ELSA Speak, Rosetta Stone, LingQ AI-based language learning tools increase fluency by 25% [49-50] Assessment & Feedback Automation AI-powered assessment tools for grading. Automated quiz generation with explanations. AI-driven assessments with performance analytics. Multimodal assessment feedback with AI recommendations. Gradescope, Turnitin, Otter.ai, Formative, Prodigy AI assessment tools improve grading accuracy by 40% [131] Faculty Assistance in Teaching AI-assisted content planning and syllabus design. Generates detailed lesson plans and academic reports. AI-driven recommendations for diverse learning materials. Automated course development and content suggestions. Canvas AI, Blackboard Learn, Google Classroom, Moodle, Edmodo AI in faculty teaching support enhances efficiency by 35% [76] Ethical Concerns & Bias Potential bias in AI-generated content. Issues related to misinformation and biases in learning models. Concerns over ethical AI use and fairness. Bias mitigation strategies implemented but still evolving. Explainable AI (XAI), Fairness Indicators by Google, AI Ethics Toolkit Ethical AI challenges remain a key research area in EdTech [66-67] Data Privacy & Security Advanced security protocols for AI-based learning. Risk of data breaches and academic misuse. Ensures user privacy with AI-driven encryption. AI-driven security mechanisms for user protection. GDPR-Compliant AI tools, Safe Exam Browser, Privacy-Preserving AI AI privacy concerns impact adoption rates by 20% [13-14, 27-28] Impact on Traditional Teaching Roles Assists educators but does not replace them. Blended learning approach with teacher augmentation. AI-powered virtual teaching assistants. AI enhances but does not eliminate traditional teaching roles. TutorMe, Cognii, Knewton, Squirrel AI, AI Teaching Assistants AI-assisted learning enhances, rather than replaces, educators [41, 131] Support for Different Learning Models Supports research-driven learning. Works well in flipped classroom models. Optimized for blended and hybrid learning. Stronger support for multimodal learning. Google Classroom, Edmodo, Microsoft Teams for Education, Zoom AI, Moodle AI AI supports all modern teaching methodologies [50, 89-90] AI-based applications have emerged as a disruptive force, a game changer in several sectors, including education, industry, cyber security, business applications, data storage, corporate processes, analytics, interactive platforms, communication systems, and social media-related systems. Recently, AI-based Learning Tools (AI-LTs) have significantly influenced business and education, among other fields of life. These tools, demands, and developments have radically changed how experts, students, and faculties in higher education think, learn, work, and survive. According to [3, 20, 36], a continuous blending of AI and humans creates hybrid systems. AI-based learning tech tools have the vast potential to improve decision-making, automate administrative tasks, decrease workloads, provide immediate feedback, customize learning experiences, and improve student engagement. Despite the initial slow pace of acceptance, educators expect the use of AI-powered technologies in higher education to grow [4, 7, 13, 16, 26, 37-39, 120]. AI has the potential to solve significant problems in higher education and spur innovation in methods of instruction and learning [3, 7, 10, 12, 15-18, 39, 44-54]. Intelligent tutoring systems, chatbots [42, 63], adaptive learning platforms, automated grading systems, and data analytics tools are some examples of educational learning that utilize AI-based technologies [3, 16, 89]. The AI-based Learning Tools (AI-LTs) will be practical to educational stakeholders in higher education only by their basic understanding of AI-based Learning Tools (AI-LTs) availability and easy usability [86-88]. Further, in addition to these advantages, these GPT-(AI)-based tools in higher education raise several issues and worries, including algorithmic bias and discrimination [21-22, 72-74, 82-84, 130], data privacy and security [13, 14, 27, 57-58, 121], and found several ethical issues [77, 83, 89, 114-115, 119, 122, 127-128, 135, 137,144-160]. This review narrows its primary focus to AI-based tutoring systems, adaptive learning platforms, and faculty engagement tools, which represent the most practical and impactful applications of AI-LTs in higher education. While the broader spectrum of AI-LTs is acknowledged, the analysis emphasizes these areas to offer a more in-depth evaluation and actionable insights for educators and policymakers. 2. Literature Review In the AI-based Learning Tools (AI-LTs), research studies have shown as in Table 1-4 that the effect on higher education has grown significantly in recent years [1-12, 15-19, 25-26, 29-32, 47-59], it supports that AI supported education improves the learning outcomes in higher education significantly as shown in Figure1. These tools contribute notably to improved learning outcomes, adaptive content delivery, and increased learner engagement. As illustrated in Figure 1, the AI-Enhanced Personalized Learning Model synthesizes how intelligent algorithms, learner profiling, and real-time feedback loops collectively drive personalized academic support, thereby reinforcing the observed improvements in student achievement and pedagogical efficiency. Researchers from a wide range of disciplines, such as computer science, psychology, ethics, and education, have investigated different aspects of implementing AI-based tools in higher education to improve the efficiency of systems. According to [36-37], when examining the top 50 AI studies in higher education, there is a general emphasis on forecasting students' learning status, specifically academic achievement, student models, and dropout and retention ratios. Higher education research, however, noticeably undervalues cooperation, communication, self-efficacy, higher-order thinking abilities, and AI capabilities [36-37]. According to [86-88], research on AI in higher education is still in its infancy. Table 2 presents a detailed comparative analysis of prominent AI-based learning tools (AI-LTs) such as DeepSeek, ChatGPT, Meta AI, Gemini, and other emerging platforms used in higher education. The analysis evaluates each tool across multiple critical dimensions relevant to academic environments, including learning personalization, student engagement, adaptive learning capabilities, support for critical thinking, writing and research assistance, language and communication enhancement, and assessment automation. Additionally, the table considers how each tool supports faculty workflows, addresses ethical concerns (such as algorithmic bias and transparency), ensures data privacy and security, and impacts traditional teaching practices. The final metrics assess how well these tools align with contemporary teaching models, providing a holistic view of their practical utility and integration potential within higher education systems. Table 2 : Comparative Analysis of Existing AI-LTs ( Deepseek,CHatGPT,Meta AI, Gemini AI, other AI based tools etc.) for Higher Education. Learning Metric DeepSeek (2023) ChatGPT (2022-Present) Meta AI (2023) Gemini AI (2023-Present) Other AI-Based Learning Tools (2022-Present) Key Findings & Referenced studies Personalized Learning AI-driven contextual learning. Adaptive, real-time tutoring. Personalized content recommendations. Multimodal personalized learning. Socratic AI, Squirrel AI, Knewton, DreamBox Learning AI improves learning outcomes by 30-40%. [3-20, 40-60] Student Engagement Interactive Q&A with research support. High engagement in conversations and discussions. Gamification and AI-driven feedback. Text, image, and speech-based interaction. Nearpod, Kahoot, Edpuzzle, Flipgrid, Wooclap AI enhances student engagement by 25-35% [40-60] Adaptive Learning Context-aware recommendations. Dynamic adaptation to student responses. AI-powered intelligent tutoring systems. Real-time AI analysis for personalized learning. Smart Sparrow, Carnegie Learning, ALEKS Adaptive AI boosts retention by 20% [4, 16, 26, 43-47, 60, 84, 89, 110] Critical Thinking Development Supports deep inquiry and logical reasoning. Enhance analytical discussions. AI-generated insights for cognitive learning. Logical reasoning and advanced analytics. IBM Watson Education, Thinkster Math AI fosters 18% improvement in critical thinking [1-20, 30-50, 70-90] Writing & Research Assistance Generates academic summaries. Supports structured writing and citations. AI-driven literature synthesis. AI-enhanced writing with multimodal support. Grammarly, QuillBot, Scite, Zotero, Mendeley AI improves research efficiency by 30% [56-70, 93, 105-109, 121-130] Language & Communication Multilingual support for academic research. Natural language processing for communication. AI-driven speech synthesis and learning. Translation and linguistic AI. Duolingo, Babbel, ELSA Speak, Rosetta Stone AI accelerates language fluency by 25%. [99, 11, 68, 10-133] Assessment & Feedback AI-driven grading systems. Automated quiz generation with explanations. AI-based academic performance tracking. AI-enhanced assessment with personalized feedback. Gradescope, Turnitin, Otter.ai, Formative AI improves grading accuracy by 40%. [30-31, 34-35, 75, 98, 101, 103] Faculty Assistance AI-generated course materials. Automated lesson planning and recommendations. AI-driven academic content suggestions. Content creation and syllabus planning. Canvas AI, Blackboard Learn, Google Classroom, Moodle AI enhances faculty efficiency by 35% [30-31, 34-35, 75, 98, 101, 103] Ethical Concerns & Bias Potential biases in AI training datasets. Risks of misinformation and content biases. Ethical AI fairness concerns. Bias-mitigation strategies evolving. Explainable AI (XAI), Fairness Indicators, AI Ethics Toolkit Ethical AI remains a key challenge in education [2, 15, 21, 22, 28, 41, 51, 64, 65, 69, 77, 83, 89, 114, 115, 119, 122, 127-128, 135, 137,144-160] Data Privacy & Security AI-driven data protection measures. Risks of data breaches and misuse. Ensures user privacy through encryption. AI-driven privacy protection mechanisms. GDPR-Compliant AI, Safe Exam Browser AI privacy concerns impact adoption by 20% [13-14, 27-28] Impact on Traditional Teaching Augments but does not replace teachers. AI-assisted blended learning approaches. AI-powered virtual teaching assistants. Complement educators rather than replacing them. TutorMe, Cognii, AI Teaching Assistants AI enhances rather than replaces educators [1, 8, 19, 29-32, 60, 61, 71, 123] Support for Teaching Models Effective for research-based learning. Suitable for flipped classrooms. Optimized for blended and hybrid learning. Strong multimodal learning support. Google Classroom, Microsoft Teams for Education, Moodle AI AI supports all modern teaching methodologies [1], [7, 10, 39, 85, 98, 105-106, 124, 138] Further, innovations in education, recognizing AI's potential to transform the educational system and increasing efficiency, investigate the pedagogical implications of AI base tools in higher education [4-15, 79-85, 138]. AI-based Learning Tools (AI-LTs ) integration can increase productivity and revolutionize educational systems. The management, administration, student recruitment, and pedagogical processes in international higher education, online & remote learning, will improve sustainability and development [55-56]. To change the ways of learning interactions from conventional to machine-focused & knowledge-centered methodologies, focus on learners' needs and the role of AI-based Learning Tools (AI-LTs ) in the education system [70-71, 91-92]. Several studies investigate how AI-based tools, such as adaptive learning platforms and intelligent tutoring systems, lead to improving academic performance, increasing student motivation, and enhancing personal learning [4-15, 36-40, 44-48, 94, 110]. The results of [84-85] indicate the importance of AI-based Learning Tools (AI-LTs) enhancing learning outcomes, particularly in developing students’ skills, fostering group work, and making a more effective research environment. Another point [120-123] brought out is that AI systems could be applied to online education to enable large-scale, individualized interactions between learners and instructors. According to studies [79-80], AI tutoring systems significantly improve student performance outcomes in higher education. Moreover, the nature of higher education learning revolutionizes some fields, such as boosting access, retention, enhancing learning, improving instruction, assessment, feedback, trimming costs, time, and administration management [1-2, 4-15,19, 36-40, 94, 113,126-128]. Table 3: Details of key parameters in learning factors Category Factor/Parameter Definition & Impact Studies Personalized Learning Adaptive Learning Systems AI tailors content based on student needs and progress. [3, 20, 40-60] AI-Powered Tutoring AI provides real-time personalized guidance. Learning Path Optimization AI predicts the best learning paths for students. Student Engagement Gamification & Interactive Learning AI-driven engagement techniques boost retention. [40-60] AI-Assisted Discussion Boards AI moderates and enhances discussions. Virtual Reality (VR) & Augmented Reality (AR) AI integrates VR/AR for immersive learning. Adaptive Learning AI-Based Recommendations Personalized study materials based on learning style. [4, 16, 26, 43-47, 60, 84, 89, 110] Intelligent Feedback Systems AI generates automated feedback to students. Real-Time Performance Analytics AI tracks and reports student progress. Critical Thinking AI-Powered Research Tools AI aids in deeper analytical learning. [1-20, 30-50, 70-90] Cognitive Skill Enhancement AI assists in problem-solving exercises. AI-Supported Debate Training AI fosters argumentation and logic-building skills. Assessment & Feedback AI-Based Grading Systems AI automates grading and analysis. [30-31, 34-35, 75, 98, 101-103] Intelligent Exam Proctoring AI ensures exam security and fairness. Personalized Feedback Generation AI customizes feedback for students. Faculty Assistance AI-Assisted Curriculum Development AI helps in designing course content. [30-31, 34-35, 75, 98, 101, 103] Automated Content Generation AI auto-generates quizzes, assignments, and study materials. Digital Teaching Assistants AI helps faculty manage classes efficiently. Ethical Concerns Algorithmic Bias & Fairness AI biases in data affect learning outcomes. [2, 15, 21-28, 41 ,51, 64 -69, 77, 83, 89, 114-122, 127- 137] Data Privacy & Security Risks AI in education must ensure user privacy. Transparency in AI Decisions AI’s role in grading must be explainable. The study [3] indicates a favorable trend toward incorporating AI learning tools into learning environments, attributed to the technology's recognition as cutting-edge teaching. According to [70-71], [91-92], AI teaching systems positively impact college students' environmental education. Table 4: Metrics for Learning Outcome Assessment in AI-Based Higher Education. Metric Definition & Relevance AI’s Role in Enhancement Studies Knowledge Retention Measures how well students remember course material. AI-driven spaced repetition and adaptive learning. [74] Student Performance Analyzes grades, test scores, and skill improvement. AI-based analytics track trends and weaknesses. [25, 26] Engagement Rate Tracks student participation in activities. AI monitors discussion forums and class interactions. [16, 17] Personalized Learning Score Measures how well AI adapts to student needs. AI-driven dynamic content recommendations. [41, 66, 67] Time to Competency Assesses how quickly a student master’s a topic. AI accelerates learning with focused materials. [49, 50] Critical Thinking Score Evaluates problem-solving and reasoning skills. AI fosters cognitive development via challenges. [131] Assessment Accuracy Measures the reliability of grading and feedback. AI reduces grading errors and subjectivity. [75] Dropout Prediction Identifies students at risk of failing. AI analyzes data to suggest early interventions. [41] Furthermore, AI-based Learning Tools (AI-LTs ) can potentially transform social relationships in higher education, affecting students, instructors, and technology systems [42-48, 110]. The [42-48] studies provide evidence of the beneficial effects of an interactive chatbot on the motivation and engagement of engineering students. According to Essel et al. (2022) [63], students who interact with chatbots [42, 63] or virtual teaching assistants perform better academically. According to [42, 63], AI-based chatbots have a favorable effect on teamwork and learning outcomes. Recent developments in virtual and immersive learning platforms provide new directions for AI-based education. Prasetya et al. (2023) [161] demonstrated the effectiveness of metaverse-based virtual laboratories in enhancing students’ cognitive and practical skills. Muskhir et al. (2024) [ 162] offered a comprehensive bibliometric analysis of virtual reality applications in vocational education, while Samala et al. (2024) [163] explored global trends and future challenges in educational technology integration. These studies further support the growing role of emerging AI-powered tools in reshaping higher education. Sambo et al. (2025) [164] conducted a systematic review on barriers to gamification adoption in education, identifying cultural, technological, and institutional challenges that hinder the effective implementation of educational innovations in developing countries. Their findings are relevant for understanding potential systemic obstacles in AI-LTs deployment, particularly in resource-constrained settings 2.1 Analytics for learning and assistance for students. The fields of AI-based Learning Tools (AI-LTs) have support in analytics and student innovation in education. Using student data and analytics to improve learning outcomes and educational experience has become more popular [18, 70-71, 79, 85, 91-92, 138]. AI-based learning technologies offer benefits in identifying at-risk students, suggesting tailored interventions, and enabling prompt feedback and assessment by enabling real-time analysis of massive amounts of data related to students' emotions as well as their learning [132-133, 139-142,144-160]. AI-driven learning analytics and student support systems can improve student progress and give educators valuable insights [109-111]. According to [52-54], machine-learning approaches successfully identify contextual characteristics that differentiate high-achieving and low-achieving pupils in reading proficiency. In a higher education context, [91-94] AI-based genetic algorithm grouping method performed better than traditional grouping methods. Ouyang et al. [109-111] explored how the groups collaborated within online interaction environments using AI algorithms and learning analytics. 2.2 Evaluation and grading. Another interesting area of exploration is the ability of AI-based Learning Tools (AI-LTs) to automate grading and assessment processes. Researchers study the potential benefits and drawbacks of automated grading, as well as the validity and reliability of AI-based grading systems when compared with traditional human grading methods [29-35, 97-105]. Natural language processing (NLP) and plagiarism detection are two examples of AI in assessment that can automate grading, lessen burden, and facilitate data-driven decision-making [29-35, 97-105]. 2.3 The professional development of educators. AI-based Learning Tools (AI-LTs) support innovative pedagogy in teaching methodologies and frameworks as shown in Figure 2 , described the details of AI-integrated framework in higher education system. Figure 2 provides a comparative analysis between traditional education models and AI-supported approaches, highlighting key shifts such as data-driven personalization, real-time feedback, intelligent content delivery, and adaptive learner support. This comparison underscores how AI integration enhances instructional flexibility, learning efficiency, and educational outcomes within modern higher education systems. This research focuses on how AI technologies can support the development of teachers' positive attitudes and perceptions, flexible teaching methodologies, and personal learning experiences. It also covers the impact of AI on teachers' professional development [4-15, 29-35]. As a result of collaboration between institutions, legislators, and AI developers to address the integration problems about educational aid and the kind of support educators need in integrating AI technologies into their methods of teaching [4-15]. Interdisciplinary collaborations are required for comprehensive research and development on morality in AI-driven learning tools [52-54, 92-94, 135]. In AI-based learning environments, researchers focus on algorithmic bias, discrimination, justice, transparency, and accountability [126-128]. Figure 3 illustrates a focused framework for the integration of AI-based learning tools (AI-LTs) in higher education, emphasizing three core domains: administration, teaching, and learning. Within administration, AI supports data-driven decision-making, resource optimization, and predictive analytics. In the teaching domain, it enhances instructional design, automates content delivery, and provides real-time feedback. For learning, AI enables personalized learning paths, adaptive assessments, and continuous learner engagement. Together, these components demonstrate how AI-LTs can holistically enhance the operational and academic dimensions of higher education institutions. 2.4 Ethical Considerations Ethical considerations in deploying AI technologies, including ensuring equity and inclusivity, and striking a balance between human teachers and AI tools, are considered. The impact of AI on society must be examined in detail, including changes in employment trends and the transformation of the workforce [19, 33-35, 95-96] and privacy violations, agency, responsibility, and surveillance [123]. Moreover, to address ethical concerns such as algorithmic bias, privacy violations, and the erosion of human-centric teaching, institutions should develop and implement clear ethical guidelines for AI integration. This includes transparent algorithmic decision-making, student consent protocols for data usage, and faculty training programs that emphasize human-AI collaboration rather than substitution. Institutional policies must prioritize fairness, inclusivity, and accountability to ensure responsible AI adoption in educational environments. 2.5 Research Questions The study finds the answers to the following questions. How do AI-based Learning Tools (AI-LTs) affect learning in higher education? What is the role of AI-based Learning Tools (AI-LTs) in higher education for students? How do AI-based Learning Tools (AI-LTs) impact higher education students, faculty, and researchers? 3. Methodology This critical review employs a systematic approach to evaluating existing research to understand the impacts and find the challenges of AI-based Learning Tools (AI-LTs), like DeepSeek, GPT_4, Gemini, etc. The methodology is structured to ensure a comprehensive and unbiased investigation of the relevant literature, addressing the strengths and limitations of current findings. This study has adopted a qualitative approach for collecting and analyzing electronic databases to identify peer-reviewed articles published in English since 2020. Given the diversity of methodologies, reporting styles, and outcome variables across the included studies, a quantitative meta-analysis was not feasible in this review. The improvement percentages cited (e.g., 30–40% better learning outcomes) are extracted from individual studies and are not statistically calculated within this paper. Future research should focus on quantitative synthesis and meta-analysis to provide robust statistical confirmation of AI-LTs impacts. 3.1 Research Design The research follows a qualitative, exploratory design using a systematic literature review method. This design is suited to synthesize existing studies, identify thematic patterns, and evaluate trends related to AI-LTs such as ChatGPT, DeepSeek, Meta AI, and Gemini within higher education. The review aims to answer three guiding questions: What is the impact of AI-LTs on student learning outcomes? How do faculty utilize AI-LTs for instructional enhancement? What ethical and societal challenges arise from the adoption of AI in academia? 3.2 Study Selection and Data Extraction This study adopts a qualitative, exploratory research design grounded in a systematic literature review approach. The goal is to synthesize current trends, impacts, and ethical challenges associated with AI-based learning tools (AI-LTs) in higher education. The methodology was carefully structured to ensure transparency, rigor, and reproducibility, aligning with best practices in evidence-based educational research. The research design was guided by three primary objectives: first, to explore how AI-based tutoring systems and adaptive learning platforms influence student learning outcomes; second, to investigate how faculty utilize AI-LTs for enhancing instructional practices; and third, to critically examine the ethical and societal challenges that accompany the integration of AI in academia. This design was appropriate given the emergent nature of AI technologies and their multifaceted role across educational systems. 3.3 Search Strategy A comprehensive literature search was conducted using major academic databases including Scopus, Web of Science, Google Scholar, ERIC, SpringerLink, and IEEE Xplore. The search employed relevant keywords and phrases such as “AI in education,” “AI-based learning tools,” “ChatGPT in higher education,” “DeepSeek,” “adaptive learning,” “AI tutoring systems,” “AI ethics in education,” “Meta AI,” “Gemini AI,” and “LLMs in teaching.” Boolean operators were used to combine terms where appropriate. The search was limited to peer-reviewed sources published between January 2020 and May 2024 to ensure that the analysis reflects recent advancements and current educational practices influenced by AI. Clear inclusion and exclusion criteria were defined to guide the selection of studies. Included studies were those that focused specifically on the application of AI tools in higher education contexts, were published in peer-reviewed journals or conference proceedings, and addressed themes such as pedagogical impact, learning outcomes, or ethical dimensions. Studies were excluded if they centered solely on primary or secondary education, were non-English publications, or lacked full-text access or methodological transparency. 3.4 Study Selection Process The study selection process followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The review process involved four stages: identification, screening, eligibility, and inclusion. Initially, 312 records were retrieved. After title and abstract screening, followed by full-text eligibility review, a total of 92 studies were selected for final inclusion. The flow of this process is illustrated in the PRISMA diagram provided in Figure X. To ensure systematic and consistent data collection, a structured data extraction framework was applied. For each included study, relevant details were recorded such as the study title, year of publication, the specific AI tool analyzed, the educational setting, reported outcomes (e.g., engagement or personalization), ethical concerns (if mentioned), and the platform or model (e.g., ChatGPT, DeepSeek, Gemini) discussed. Data extraction was carried out independently by two researchers, and any disagreements were resolved through discussion with a third reviewer. 3.5 Quality Assessment (SALSA Framework) The methodological quality and relevance of each selected study were assessed using the SALSA framework, which comprises four phases: Search, Appraisal, Synthesis, and Analysis. The search strategy was methodologically robust, while appraisal involved evaluating each study based on relevance, clarity, and risk of bias. Synthesis involved identifying themes and patterns across studies, and analysis focused on drawing meaningful conclusions aligned with the objectives of this review. 3.6 Data Synthesis Approach The data synthesis approach relied on thematic analysis, where recurring concepts and themes were categorized and analyzed across the selected studies. Where studies reported quantitative improvements, such as “30–40% better personalized learning outcomes,” these values were directly extracted from the original sources. It is important to note that such percentages are not the result of meta-analytical averaging but are based on self-reported or empirically observed outcomes in individual studies. 3.7 Ethical Considerations Ethical considerations were carefully incorporated into the review process, even though this study relies on secondary data. The analysis identified key ethical concerns associated with AI deployment in higher education, including algorithmic bias, data privacy, surveillance risks, and issues of equity and access. Additionally, the review highlights how AI may alter traditional teacher-student dynamics, necessitating professional development programs to help educators and learners adapt responsibly. A dedicated subsection has been included in the discussion to consolidate and reflect on these ethical challenges. In the methodological framework, we ensure both breadth and depth in understanding the multifaceted impact of AI-LTs in higher education. It balances technical rigor with ethical sensitivity, offering a foundation for robust analysis and meaningful academic insights. The SAMR model [164] classifies technology use in education into four levels: Substitution (technology acts as a direct tool substitute with no functional change), Augmentation (technology acts as a substitute with functional improvements), Modification (technology allows significant task redesign), and Redefinition (technology enables previously inconceivable tasks). AI-based Learning Tools like adaptive tutoring systems typically fall between the Modification and Redefinition levels, as they fundamentally reshape learning processes and offer capabilities beyond traditional teaching methods In this study we adopted the SAMR model (Figure 4) (Substitution, Augmentation, Modification, Redefinition) to evaluate the integration of AI-based Learning Tools (AI-LTs) in higher education. This model helps classify AI-LTs applications based on their depth of technological integration and educational impact, offering a systematic framework to assess their role in transforming teaching and learning processes. The selection of related published papers, following the initial search, involved two reviewers screened the titles and abstracts of articles independently to determine their relevance to the inclusion and exclusion criteria. It was also considered that disagreements were resolved through discussion and, if necessary, consultation with a third reviewer. The selected studies and full-text articles were retrieved. This review narrows its primary focus to AI-based tutoring systems a gainst the inclusion and exclusion criteria. A standardized data extraction format was used to collect information on the respective study as per study characteristics (like author, year, country), sample characteristics (like sample size, age range), and key findings related to the role of AI-based Learning Tools (AI-LTs) in higher education. Two reviewers extracted data, and discrepancies were justified for their selection by discussing independently. 4. Results & Discussion & Findings of the review study In this section, we discuss the findings of the study according to research questions in sequence: 4.1 AI-based Learning Tools’ (AI-LTs) effect on learning in higher education i. Growth of AI Research in Higher Education The number of studies on AI tools in higher education have grown significantly in recent years [4-15], [23-24], [36-40]. Studies [36-40] analyzed the top 50 AI-related studies in higher education, focusing on predicting important student outcomes like academic achievement, student retention, and dropout rates. However, these studies often overlooked key areas like collaboration, communication, self-efficacy, and higher-order thinking abilities. These aspects are essential for student success and were widely ignored in the research, suggesting the potential gap in the current AI-based Learning Tools (AI-LTs) role in higher education. ii. Innovations in Teaching and Learning AI Tools for Learning Enhancement: The learning methodologies, like adaptive learning platforms, intelligent tutoring systems (ITS) or AI-based Learning Tools (AI-LTs), have great potential to change personalized learning and improve academic achievements in higher education. According to [4-15], these AI-based technologies rely on algorithms that analyze students' learning patterns and instructional content so these can be updated in real-time. This approach helps learners receive tailored resources and support to grade their progress and create a more effective learning environment. Personalizing content delivery allows adaptive learning systems to bridge understanding gaps among learners, especially students with diverse needs or those who may fail in the regular classroom environment. According to [36-40], [94], AI learning tools promote learning experience through personalized feedback. The interactive feedback loop ensures students do not fall behind, as they receive support precisely when needed. ITS-AI-based systems can also trace long-term academic trends and support instructors with data to improve teaching strategies. According to [79-81], AI tutoring systems improve student performance, develop confidence and allow them to work at their own pace. Learners who used AI-driven tutoring platforms performed better than their peers in traditional systems. These systems, designed to offer personalized feedback and adapt to individual student needs, ensure that learners receive consistent support, especially in areas where they struggle. Online Learning: According to [120-123], AI-based Learning Tools (AI-LTs) have transformative capabilities on a large scale among learners. Further, the authors presented how AI-LTs are used in managing and analyzing vast amounts of data related to students in developing a personalized learning mechanism. These systems allow instructors to handle larger classes but still allow for a high degree of personalization and are, thus, an essential tool for developing online learning environments. According to [91-92], AI-LTs contribute to fostering knowledge-centered approaches in education, significantly enhancing students' interest in learning. Further, more dynamic and interactive learning environments can be created using AI-LTs, active participation, and deeper learning. This methodology supports knowledge-centered teaching, which aims for deep engagement with subjects and encourages the critical exploration of concepts rather than the passive intake of information. Therefore, AI-LTs help create a student-centered environment in which students are motivated to interact with the subject problems to understand questions better. Practical Outcomes: According to [55-56], AI-based system learning tools are vital in enhancing administrative efficiency and sustainability in higher education systems globally. They found it time and effort saving, like enrollment, queries about students, and scheduling. AI-driven systems can automate these functions, ensuring faster and more accurate data volumes, a primary requirement for institutions. In the international education system, these tools will overcome the complexities of regulations, logistics, and communication problems. These AI-LTs help institutions manage resources more effectively. Data-driven insights from AI systems also enable informed decisions about students' needs, trends, and outcomes. A data-centric approach supports long-term sustainability in the AI-based education system, allowing institutions to evolve in line with the dynamic changes occurring around the globe while still maintaining the highest standards of education and streamlining operations. iii. Learning Analytics and Student Support a. AI-driven learning analytics According to [139-142], using AI-driven learning analytics provides real-time feedback on both emotional states and learning progress, an essential development in the learning, understanding, and support of students in a broader way. Further, the study discusses that AI-based learning systems, from facial expressions to body language and even tone of voice, might analyze excess data points to effectively determine the learner's emotional state amid learning activities. The AI-driven system provides immediate feedback on academic and psychological well-being that allows the detection of stress or disengagement by giving AI-based stimuli to make a personalized intervention to improve the quality of education. This methodology of real-time emotional feedback helps create a more responsive learning environment, allowing educators to address emotional barriers to learning and provide timely support before minor issues become significant obstacles to academic success. Moreover, learning analytics based on AI is pivotal in offering evidence-based interventions tailored to at-risk students; research has proven that this individualized intervention helps improve retention rates and empowers students. b. AI-based Learning Tools (AI-LTs) - collaborative efforts In the studies [91-94] emphasized that the AI-based grouping methods significantly outperform traditional methods (random selection or instructor-based grouping) in collaborative learning approaches. Students working in AI-constructed groups were more communicative, completed more tasks, and shared the workload more equitably. These results indicate that AI-based learning can improve the quality of group work by facilitating better collaboration, deeper engagement, and more optimal learning outcomes. Further studies [109-111] extend an idea of AI-enhanced collaboration by emphasizing its effect on online group interactions. The research shows that AI-driven tools improve collaborative group outcomes in virtual learning environments more effectively in communication, coordination, and problem-solving phases. AI-based learning systems can monitor online discussions and interactions, providing feedback or nudges to encourage active participation and encourage group members to stay on task prompts to stimulate further discussion and helping teachers intervene at the appropriate moment of need. 4.2 Findings on the Moral and Societal Ramifications of AI in Higher Education i. Pedagogical Implications and Innovations AI-powered tools, such as DeepSeek, GPT, and Gemini for adaptive learning and intelligent tutoring systems, improve learning outcomes by personalizing education and fostering student engagement [44-48, 94, 110]. AI-based learning systems can adapt the latest tools enhanced to learners' needs, improve academic results, and encourage collaboration [79-81]. ii. Learning Analytics and Student Support The AI-based Learning Tools (AI-LTs) and analytics identify the students who are likely to fail and offer interventions at the right time [132-133, 139-142]. Studies [52-54] used machine learning methodologies to predict contextual factors affecting academic performance with a high success ratio. Table 5 :The Role of AI in Learning: Higher Education Findings in Literature Studies Focus Area Key Findings [36-39] Top AI studies in higher education Forecasting student outcomes: lack of focus on collaboration and communication. [79-81] AI tutoring systems Significant improvements in student performance. [91-94] AI-based grouping for collaboration Improved group performance compared to traditional methods. [52-54,94] Machine learning for reading proficiency Identification of contextual factors for student success. [59-62] Machine learning for teacher feedback analysis Improved teaching strategies based on student feedback. [120-123] AI in online learning Large-scale personalization and ethical concerns like surveillance. iii. Educator Development and AI Integration AI-based tools, including DeepSeek, GPT, Gemini, etc., assist teachers in analyzing feedback and adapting teaching methods [59-62]. Institutions must address educators' concerns about AI integration and provide proper training [4-15]. The introduction of AI tools changed traditional teaching roles and created a need for balanced approaches to retain human agency and creativity in teaching [20]. iv. Automation of Evaluation and Grading AI-based tools, such as NLP-oriented AI-based Learning Tools (AI-LTs ) and automated grading systems, reduce teachers' workloads, ensure speedy feedback, and make grading much easier [29-32, 97-101]. While AI-based grading systems enhance efficiency, concerns remain about their reliability compared to human grading [33-35]. v. Societal Impact and Workforce Implications Incorporating AI into education and related fields influences job roles, necessitating reskilling and adaptation in the workforce [95-96,120-123]. The increased reliance on AI reshapes interactions between students, educators, and AI systems, raising questions about the balance between technology and human connection [4-15]. vi. AI Literacy and Awareness According to [86-88], the need to define AI literacy and create accessible materials for non-experts to better understand the limitations in the education field is essential. Interdisciplinary research is also essential to address ethical concerns, improve transparency, and develop responsible AI practices [20]. Table 6 :Key Facts of Learning and Findings Focus Area Key Findings studies AI in Learning Analytics Real-time data analysis identifies at-risk students, enabling tailored interventions. [139-142] Student Performance AI tutoring systems boost overall academic performance [79-81] Ethical Concerns Algorithmic bias and privacy risks demand ethical frameworks [120-123, 126-128] Collaboration AI-based grouping methods enhance team effectiveness [91-94] Efficiency in Assessment AI automates grading, reducing educator workload [29-35] Educator Support AI aids in professional development by personalizing teaching strategies [59-62] Workforce Transformation AI-driven changes in education impact job roles and reskilling requirements [95-96] These findings underscore AI's potential to revolutionize higher education while highlighting ethical, societal, and pedagogical challenges that demand careful consideration and collaboration across disciplines. Case study The studies [52-54], [94] presented how machine learning algorithms can effectively differentiate between high- and low-achieving reading proficiency, with valuable insights to be drawn toward student support. This proactive methodology enables educators to implement target interventions, such as additional resources for low-achieving students and further challenges to high-achieving students. The use of machine learning keeps on scoring and following trends in students' reading performance over a period, allowing detection of trends and hints at early signs of academic struggle. The approach reflects growing AI and machine learning roles transforming educational practices as they facilitate even more targeted and evident-based interventions. iv. Automated Assessment and Grading According to [29-32], [97-101] studies, AI tools, especially Natural Language Processing (NLP) applications, have excellent benefits in grade automation and assessment efficiency. They reveal that AI-based learning applications in grading enable instructors to minimize their efforts while increasing accuracy and reliability during evaluation. NLP-AI-based applications can accurately grade written content through OCR methods, such as essays, reports, and open-ended responses. The NPL-based tools provide accurate support by analyzing various linguistic features, including grammar, syntax, and coherence. These tools reduce human bias, ensure standardized grading, and save instructor’s valuable time. According to [33-35] studies, AI helps in plagiarism detection and data-driven decision-making, giving some insight into rewriting the book on academic integrity and educational management. AI-powered plagiarism detection tools are becoming increasingly sophisticated as they use machine learning (ML) algorithms to detect subtle forms of content duplication and citation issues. There is a significant reduction in the likelihood that cheating will remain hidden, allowing academics to ensure proper standards. Furthermore, AI enables the aggregation and analysis of student performance data in new ways. It also finds patterns among students' behaviors, engagement levels, and academic performance, offer insights for educators and administrators to implement in curriculum designs, learning methods, and institutional policies. v. Educator’s Professional Development-Flipped & Blended Learning The studies [59-62] suggest that AI-based Learning Tools (AI-LTs) will make teachers' professional development much easier; it may facilitate flexible forms of teaching that diversify resources in comparison with conventional methodologies to teachers. AI can scrutinize the interaction within a class, results in learning and students' interests, and offers more suitable, person-specific teaching models and materials for diverse classroom environments. In this way of AI-based learning, the teacher has enough time to implement flipped classroom models or blended learning, and this is all done by giving a lot of administrative and grading time back to teachers. According to [4-15] studies, an effective AI integration into educator professional development depends on collaboration between educational institutions, policymakers, and AI developers. As AI-based research continues to reshape teaching and learning environments, educators must be trained to use AI tools in their classrooms meaningfully. Therefore, the study emphasized collaboration in developing frameworks for AI training programs so that teachers are equally acquainted with the technical aspects of AI to enhance learning outcomes. vi. Social and Ethical Implications According to [4-15, 126-128] studies, several significant challenges in integrating AI technologies into education include algorithmic bias, discrimination, and privacy violations Studies [120-123] deal with the ethical dilemmas that arise to balancing human and AI roles in education. Though it may enhance educational practices, AI poses a looming threat to the diminishing role of human educators. Furthermore, studies [18, 135-137] suggest solutions to these challenges by promoting human-centered AI-based application values and fostering interdisciplinary collaboration. According to a study [113], AI-based transformative impacts on the labor force and changes in employment trends. The paradigm shift toward more technology-driven roles will demand rethinking skills development and educational curricula, respectively. According to studies [36-40], researchers discuss investments in education, retraining programs, and policies that encourage the development of AI systems to mitigate the adverse effects of workforce disruption which may displace some jobs, providing opportunities for more creative, strategic, and interdisciplinary work to reshape the economy, as detailed in Table 5. The increasing usage of AI-based tools in academia and industry critically focuses on concerns regarding systems' fairness, discrimination, and transparency [118-119, 126-128], the details shown in Table 6. The researchers recommend ethical frameworks to mitigate algorithmic bias, exclusion, and inequality in AI-based applications for learning outcomes in the higher education system [4-15, 135]. According to [120-123] studies, there is a potential threat of privacy invasion globally, and the need for appropriate ethical control of AI systems. Moreover, Dignum (2017) [51] reveals the significance of AI implementation with cultural norms, human-centered values, and ethical reasoning in AI-based systems. 4.3 Future of AI-based tools like DeepSeek, GPT, Gemini etc. in Higher Education The AI forecasts students' learning status, including academic achievement, retention rates, and dropout prediction [36-40, 94] shown in Table 7. The AI systems help to define AI-based literacy and create accessible materials for non-experts to foster an understanding of all teaching methodologies [86-88]. AI-based learning systems enable real-time analysis of student data, identifying at-risk students and offering significant feedback [109-111, 139-142]. The AI-powered adaptive learning platforms and tutoring systems improve academic outcomes [44-48, 110]. Technology like AI tutoring systems and chatbots provides personalized support, boosting engagement and performance [42, 63]. AI-based learning systems promote group projects, create inclusive learning environments, and facilitate teamwork through tools like genetic algorithm-based grouping [91-94]. AI-based systems improve higher education institutions' management, administration, and recruitment processes [55-56]. Advanced systems, like AI-based Learning Tools (AI-LTs), automate grading, assessments, and plagiarism detection, reducing administrative burdens, increasing efficiency, and ensuring reliable evaluations [97-101, 103-105]. AI-based systems enhance professional growth by analyzing feedback and providing targeted training and resources. For instance, machine learning approaches like "Social Mining" help refine teaching methods based on student feedback [59-62]. Collaboration among institutions, policymakers, and developers is vital to overcome challenges and support educators in adopting AI tools [4-15]. Table 7 :Detailed key factors of learning and findings Key Areas of Learning Findings/Benefits Studies Learning Analytics Real-time student data analysis, identifying at-risk students, tailored feedback. [109-111, 139-142] Pedagogical Innovation Improved learning outcomes, engagement, and personalized learning. [4-15, 44-48, 110] Administrative Efficiency Cost and time savings through automation in grading, assessments, and management. [97-101, 103-105] Ethical Concerns Bias, discrimination, privacy, and the need for accountability in AI systems. [120-123, 126-128] Educator Development AI-supported training and development, feedback-based improvement of teaching methods. [4-15, 59-62] The findings reveal that AI-based Learning Tools (AI-LTs) are transforming the higher education system by introducing innovative teaching and learning hybrid approaches. This imbalance suggests that future studies should expand their focus on the growth of the educational field. AI-powered adaptive learning platforms and intelligent tutoring systems have remarkably succeeded in improving personalized learning and student engagement. Furthermore, AI-based systems' role in online learning and administrative efficiency enhances the scalability and sustainability of the technologies. Some Tools equipped with automated language-generating features, like NLP, have streamlined assessment processes [29-32, 97-101]. Despite the growing potential of AI-based learning tools (AI-LTs), their deployment in higher education raises significant ethical concerns. As shown in Figure 5, key challenges include algorithmic bias, surveillance-related risks, unequal access, data privacy issues, and the evolving dynamics between teachers and students. These concerns question the validity, reliability, and fairness of AI systems compared to traditional human evaluators. The figure highlights the urgent need for structured professional development programs that equip both educators and learners with the skills and awareness necessary to responsibly integrate AI-LTs into academic environments. This emphasizes the need for structured professional development programs to equip teachers and students with AI-based Learning Tools (AI-LTs) and integrated skills. AI-based tools present transformative opportunities and ethical challenges shown in Figure 5, such as algorithmic bias, discrimination, and privacy concerns. They require balancing the human and AI roles in the education field. Our study focuses on the need for human-centered AI and interdisciplinary collaboration to ensure inclusivity, transparency, ethical implications, and accountability of advanced learning tools. The findings of this study underscore the transformative potential of AI in higher education, particularly in enhancing personalized learning experiences, improving administrative efficiency, and fostering collaboration among students and educators. AI technologies can revolutionize instructional strategies, provide immediate feedback, and customize learning pathways to meet individual student needs. However, the study also highlights AI's ethical and societal challenges, including algorithmic bias, privacy concerns, and the potential dehumanization of education. This study emphasizes the importance of AI-based literacy and professional development for educators to effectively integrate AI-based Learning Tools (AI-LTs) such as DeepSeek, GPT, Gemini, Meta AI, etc., into their learning methodologies for higher education practices. 5. Limitations In the study, while we provide valuable insights into the role of AI-based tools in higher education but it has several limitations. This study is a systematic literature review relying solely on secondary data from peer-reviewed articles. The absence of primary empirical data is a key limitation, which may reduce the practical validation and originality of the findings. Future studies should incorporate empirical methodologies such as surveys, interviews, or case studies to strengthen evidence-based conclusions and validate the impact of AI-based Learning Tools (AI-LTs) in real-world higher education contexts. The selected sample size is limited to a specific number and broader domain of learning, and the findings may not be generalizable to all higher education institutions globally and specific aspects of learning. Additionally, the study relies on self-reported data, which may introduce biases based on participants' perceptions and experiences. The study also focuses primarily on the perspectives of faculty, administrators, and students without considering the views of AI developers or technology companies involved in AI tool creation. Future research could address these limitations by expanding the sample size and exploring the perspectives of other key stakeholders involved in AI implementation in higher educational institutions. 6. Conclusion This survey explored and discussed the deep-dive insights of the current AI-based Learning Tools (AI-LTs) and their impact on future trends. AI-based innovations hold immense potential to revolutionize higher education by improving learning, collaboration, and administrative processes. We analyzed the literature through existing and relevant studies to verify the impacts of these tools on higher education systems. However, the ethical, social, and pedagogical challenges associated with its integration are essential to developing higher educational systems. Educators, policymakers, and researchers must collaborate to ensure the deployment of such tools while fostering an inclusive and equitable educational landscape. This research survey refines AI-based applications and explores gaps, focusing on how the technology's transformational power of AI benefits all stakeholders in higher education. 7. Future Directions The findings suggest expanding AI applications to develop critical thinking, communication, and self-efficacy skills. Integrating AI-based Learning Tools (AI-LTs) requires technological advancements and a commitment to ethical and cultural alignment for implementations (Figure 6). Figure 6 illustrates the envisioned future of AI-based learning tools (AI-LTs) within global academia, highlighting their potential to cultivate critical thinking, communication, and learner self-efficacy. The model emphasizes the need for sustained technological innovation alongside ethical, cultural, and institutional alignment to ensure responsible and inclusive implementation. It also points toward emerging research frontiers involving AI developers, industry partnerships, and broader societal impacts—areas that warrant deeper exploration in future studies.The study may explore other remaining parameters, such as developers, industry, and social impacts, which will also be considered in future work studies. Declarations Author Contributions Statement Anwar Ali Sathio conceptualized the study, led the literature review, and drafted the initial manuscript. Prof. Dr. Muhammad Malook Rind provided critical supervision, methodological guidance, and revisions to enhance scholarly rigor. Mehboob Ali contributed to data analysis, interpretation, and manuscript editing. Ghulam Ahmed supported the design of the theoretical framework and contributed to academic validation. Sameer Ali assisted in data curation, formatting, reference management, funding, and acquisitions of resources. Competing Interests: The authors declare that they have no competing interests. Ethical Approval and Consent to Participate: Not applicable. Consent for Publication: Not applicable. Funding Statement: This research received no specific grant from any funding agency in the public, commercial, or non-profit sectors. Availability of Data and Materials: Not applicable. 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Journal of Asynchronous Learning Networks , 18 (2), n2. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-6460706","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":479146700,"identity":"7746862b-abb2-4f37-8934-b02e24150188","order_by":0,"name":"Anwar Ali 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08:22:19","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":77187,"visible":true,"origin":"","legend":"\u003cp\u003eComparative Analysis – Traditional vs AI-Supported Education\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6460706/v1/1c7691d195f14bfcd7f86d37.png"},{"id":85924955,"identity":"c5f8c1dc-bbad-48e4-8c9e-50ae60d13517","added_by":"auto","created_at":"2025-07-03 08:30:19","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":78944,"visible":true,"origin":"","legend":"\u003cp\u003eFramework of AI-LTs Integration in Higher Education\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6460706/v1/b98dfc0cde241124b88421f6.png"},{"id":85923294,"identity":"92daed17-f5db-467c-91a8-267996c7812f","added_by":"auto","created_at":"2025-07-03 08:22:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":116507,"visible":true,"origin":"","legend":"\u003cp\u003eSAMR model architecture [164]\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6460706/v1/edf8d06d1a148d2425692305.png"},{"id":85923299,"identity":"de6c5cdb-bc0b-445a-86f1-a11979901789","added_by":"auto","created_at":"2025-07-03 08:22:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":174434,"visible":true,"origin":"","legend":"\u003cp\u003eEthical Challenges in AI-LTs Deployment\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6460706/v1/f3db168ff989f785b3499aae.png"},{"id":85923295,"identity":"6d3bd683-09d4-4a22-a412-8ab1a4cc78c7","added_by":"auto","created_at":"2025-07-03 08:22:19","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":91061,"visible":true,"origin":"","legend":"\u003cp\u003eFuture Vision of AI-LTs in Global Academia\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6460706/v1/f4855d1ec73ced96727056d2.png"},{"id":92093012,"identity":"6ef39987-6d40-4a6c-aea4-5a16d2cd7c43","added_by":"auto","created_at":"2025-09-24 14:02:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2218009,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6460706/v1/89b140af-ec31-4fb9-b968-af2641c651dd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Cognitive Metamorphosis in Higher Education: AI-LTs Nexus of DeepSeek, GPT, and the Future of Scholarly Engagement","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe role of AI tools has revolutionized traditional ways of learning, teaching, and research in higher educational institutions globally. Since AI technologies have evolved and influenced global applications, teaching and learning processes in higher education are becoming increasingly creative, as shown in Table 1. \u0026nbsp; There is a significant knowledge gap in how various stakeholders in the education field, such as faculty, administrators, and students, perceive the applications of GPT-based AI tools and their potential future role globally.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe integration of AI tools has transformed traditional approaches to learning, teaching, and academic research in higher education globally. With the evolution of AI technologies such as GPT-based systems, educational practices have become increasingly innovative, adaptive, and learner-centered. As shown in Table 1, the role of AI-based learning tools (AI-LTs) is expanding across diverse domains. However, there remains a significant knowledge gap in understanding how various stakeholders, faculty, administrators, and students\u0026mdash;perceive and engage with these tools, particularly in terms of their long-term academic and institutional implications. This study specifically aims to: (1) examine the impact of AI-based tutoring systems and adaptive learning platforms on student outcomes, (2) explore faculty adoption and pedagogical enhancements via AI-LTs, and (3) critically assess the ethical and societal dimensions of AI integration. To provide contextual relevance, Table 1 presents a comprehensive comparative analysis of key AI-LTs currently shaping higher education.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 1 : Comprehensive Comparative Analysis of AI-Based Learning Tools in Higher Education\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eLearning Metric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eDeepSeek\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eChatGPT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eMeta AI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eGemini AI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eOther AI-Based Learning Tools\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eKey Findings \u0026amp; Scholarly References\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003eStudies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003ePersonalized Learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eHigh adaptability in domain-specific queries and research-based interactions.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eStrong contextual understanding and personalized tutoring features.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAdaptive content generation for various disciplines.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eMultimodal learning support with text, images, and coding.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eSocratic AI, Squirrel AI, Querium, Knewton, DreamBox Learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eAI tools provide 30-40% better personalized learning experiences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e[73,124]\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eStudent Engagement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eInteractive and dynamic response generation.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eHigh conversational engagement with real-time feedback.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eIntegrated AI-driven gamification for interactive learning.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eEnhanced engagement with multimedia capabilities.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eNearpod, Kahoot, Edpuzzle, Flipgrid, Wooclap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eAI-driven tools increase student engagement by 25-35%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e[16-17]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eAdaptive Learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eContext-aware learning and real-time data-driven personalization.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eAdjusts responses based on student progress and comprehension.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAI-powered intelligent tutors for customized learning paths.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eOffers real-time assistance with multi-faceted reasoning.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eSmart Sparrow, Carnegie Learning, ALEKS, Knowji\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eAI-based adaptive learning improves retention rates by 20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e[26]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eCritical Thinking Development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eSupports deep research-based inquiries.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eEncourages reasoning-based discussions.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eGenerates creative learning insights using cognitive AI.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eProvides logical reasoning with complex analytical capabilities.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eRaven AI, IBM Watson Education, Thinkster Math\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eAI integration in education fosters 18% improvement in critical thinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e[107-108]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eWriting and Research Assistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eStrong in AI-driven content generation for research.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eOffers structured writing support with citations.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eSummarizes and synthesizes academic literature.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eProvides multimodal citation-based writing assistance.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eGrammarly, QuillBot, Hemingway Editor, Scite, Zotero, Mendeley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eAI tools improve research efficiency by 30%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e[76]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eLanguage \u0026amp; Communication Skills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eSupports multilingual understanding and translation.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eExcellent conversational AI with natural language processing.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAI-powered real-time voice synthesis.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAdvanced linguistic models with nuanced comprehension.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eDuolingo, Babbel, ELSA Speak, Rosetta Stone, LingQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eAI-based language learning tools increase fluency by 25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e[49-50]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eAssessment \u0026amp; Feedback Automation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eAI-powered assessment tools for grading.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eAutomated quiz generation with explanations.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAI-driven assessments with performance analytics.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eMultimodal assessment feedback with AI recommendations.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eGradescope, Turnitin, Otter.ai, Formative, Prodigy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eAI assessment tools improve grading accuracy by 40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e[131]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eFaculty Assistance in Teaching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eAI-assisted content planning and syllabus design.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eGenerates detailed lesson plans and academic reports.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAI-driven recommendations for diverse learning materials.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAutomated course development and content suggestions.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eCanvas AI, Blackboard Learn, Google Classroom, Moodle, Edmodo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eAI in faculty teaching support enhances efficiency by 35%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e[76]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eEthical Concerns \u0026amp; Bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003ePotential bias in AI-generated content.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eIssues related to misinformation and biases in learning models.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eConcerns over ethical AI use and fairness.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eBias mitigation strategies implemented but still evolving.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eExplainable AI (XAI), Fairness Indicators by Google, AI Ethics Toolkit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eEthical AI challenges remain a key research area in EdTech\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e[66-67]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eData Privacy \u0026amp; Security\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eAdvanced security protocols for AI-based learning.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eRisk of data breaches and academic misuse.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eEnsures user privacy with AI-driven encryption.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAI-driven security mechanisms for user protection.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eGDPR-Compliant AI tools, Safe Exam Browser, Privacy-Preserving AI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eAI privacy concerns impact adoption rates by 20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e[13-14, 27-28]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eImpact on Traditional Teaching Roles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eAssists educators but does not replace them.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eBlended learning approach with teacher augmentation.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAI-powered virtual teaching assistants.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAI enhances but does not eliminate traditional teaching roles.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eTutorMe, Cognii, Knewton, Squirrel AI, AI Teaching Assistants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eAI-assisted learning enhances, rather than replaces, educators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e[41, 131]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eSupport for Different Learning Models\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eSupports research-driven learning.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13px;\"\u003e\n \u003cp\u003eWorks well in flipped classroom models.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eOptimized for blended and hybrid learning.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eStronger support for multimodal learning.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eGoogle Classroom, Edmodo, Microsoft Teams for Education, Zoom AI, Moodle AI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eAI supports all modern teaching methodologies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8px;\"\u003e\n \u003cp\u003e[50, 89-90]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eAI-based applications have emerged as a disruptive force, a game changer in several sectors, including education, industry, cyber security, business applications, data storage, corporate processes, analytics, interactive platforms, communication systems, and social media-related systems. Recently, AI-based Learning Tools (AI-LTs) have significantly influenced business and education, among other fields of life. These tools, demands, and developments have radically changed how experts, students, and faculties in higher education think, learn, work, and survive. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to [3, 20, 36], a continuous blending of AI and humans creates hybrid systems. AI-based learning tech tools have the vast potential to improve decision-making, automate administrative tasks, decrease workloads, provide immediate feedback, customize learning experiences, and improve student engagement. Despite the initial slow pace of acceptance, educators expect the use of AI-powered technologies in higher education to grow [4, 7, 13, 16, 26, 37-39, 120]. AI has the potential to solve significant problems in higher education and spur innovation in methods of instruction and learning [3, 7, 10, 12, 15-18, 39, 44-54]. Intelligent tutoring systems, chatbots [42, 63], adaptive learning platforms, automated grading systems, and data analytics tools are some examples of educational learning that utilize AI-based technologies [3, 16, 89]. The AI-based Learning Tools (AI-LTs) will be practical to educational stakeholders in higher education only by their basic understanding of AI-based Learning Tools (AI-LTs) availability and easy usability [86-88]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFurther, in addition to these advantages, these GPT-(AI)-based tools in higher education raise several issues and worries, including algorithmic bias and discrimination [21-22, 72-74, 82-84, 130], data privacy and security [13, 14, 27, 57-58, 121], and found several ethical issues [77, 83, 89, 114-115, 119, 122, 127-128, 135, 137,144-160].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis review narrows its primary focus to AI-based tutoring systems, adaptive learning platforms, and faculty engagement tools, which represent the most practical and impactful applications of AI-LTs in higher education. While the broader spectrum of AI-LTs is acknowledged, the analysis emphasizes these areas to offer a more in-depth evaluation and actionable insights for educators and policymakers.\u003c/p\u003e"},{"header":"2.\tLiterature Review","content":"\u003cp\u003eIn the AI-based Learning Tools (AI-LTs), research studies have shown as in Table 1-4 that the effect on higher education has grown significantly in recent years [1-12, 15-19, 25-26, 29-32, 47-59], it supports that AI supported education improves the learning outcomes in higher education significantly as shown in Figure1. These tools contribute notably to improved learning outcomes, adaptive content delivery, and increased learner engagement. As illustrated in Figure 1, the AI-Enhanced Personalized Learning Model synthesizes how intelligent algorithms, learner profiling, and real-time feedback loops collectively drive personalized academic support, thereby reinforcing the observed improvements in student achievement and pedagogical efficiency.\u003c/p\u003e\n\u003cp\u003eResearchers from a wide range of disciplines, such as computer science, psychology, ethics, and education, have investigated different aspects of implementing AI-based tools in higher education to improve the efficiency of systems. According to [36-37], when examining the top 50 AI studies in higher education, there is a general emphasis on forecasting students\u0026apos; learning status, specifically academic achievement, student models, and dropout and retention ratios. Higher education research, however, noticeably undervalues cooperation, communication, self-efficacy, higher-order thinking abilities, and AI capabilities [36-37]. According to [86-88], research on AI in higher education is still in its infancy. Table 2 presents a detailed comparative analysis of prominent AI-based learning tools (AI-LTs) such as DeepSeek, ChatGPT, Meta AI, Gemini, and other emerging platforms used in higher education. The analysis evaluates each tool across multiple critical dimensions relevant to academic environments, including learning personalization, student engagement, adaptive learning capabilities, support for critical thinking, writing and research assistance, language and communication enhancement, and assessment automation. Additionally, the table considers how each tool supports faculty workflows, addresses ethical concerns (such as algorithmic bias and transparency), ensures data privacy and security, and impacts traditional teaching practices. The final metrics assess how well these tools align with contemporary teaching models, providing a holistic view of their practical utility and integration potential within higher education systems.\u003c/p\u003e\n\u003cp\u003eTable 2 : Comparative Analysis of Existing \u0026nbsp;AI-LTs ( Deepseek,CHatGPT,Meta AI, Gemini AI, other AI based tools etc.) \u0026nbsp;for Higher Education.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eLearning Metric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eDeepSeek (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eChatGPT (2022-Present)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eMeta AI (2023)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eGemini AI (2023-Present)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eOther AI-Based Learning Tools (2022-Present)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eKey Findings \u0026amp; \u0026nbsp;Referenced studies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003ePersonalized Learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAI-driven contextual learning.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAdaptive, real-time tutoring.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003ePersonalized content recommendations.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eMultimodal personalized learning.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eSocratic AI, Squirrel AI, Knewton, DreamBox Learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eAI improves learning outcomes by 30-40%. [3-20, 40-60]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eStudent Engagement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eInteractive Q\u0026amp;A with research support.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eHigh engagement in conversations and discussions.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eGamification and AI-driven feedback.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eText, image, and speech-based interaction.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eNearpod, Kahoot, Edpuzzle, Flipgrid, Wooclap\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eAI enhances student engagement by 25-35% [40-60]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAdaptive Learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eContext-aware recommendations.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eDynamic adaptation to student responses.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAI-powered intelligent tutoring systems.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eReal-time AI analysis for personalized learning.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eSmart Sparrow, Carnegie Learning, ALEKS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eAdaptive AI boosts retention by 20% [4, 16, 26, 43-47, 60, 84, 89, 110]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eCritical Thinking Development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eSupports deep inquiry and logical reasoning.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eEnhance analytical discussions.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAI-generated insights for cognitive learning.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eLogical reasoning and advanced analytics.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eIBM Watson Education, Thinkster Math\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eAI fosters 18% improvement in critical thinking [1-20, 30-50, 70-90]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eWriting \u0026amp; Research Assistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eGenerates academic summaries.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eSupports structured writing and citations.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAI-driven literature synthesis.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eAI-enhanced writing with multimodal support.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eGrammarly, QuillBot, Scite, Zotero, Mendeley\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eAI improves research efficiency by 30% [56-70, 93, 105-109, 121-130]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eLanguage \u0026amp; Communication\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eMultilingual support for academic research.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eNatural language processing for communication.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAI-driven speech synthesis and learning.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eTranslation and linguistic AI.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eDuolingo, Babbel, ELSA Speak, Rosetta Stone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eAI accelerates language fluency by 25%. [99, 11, 68, 10-133]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAssessment \u0026amp; Feedback\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAI-driven grading systems.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAutomated quiz generation with explanations.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAI-based academic performance tracking.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eAI-enhanced assessment with personalized feedback.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eGradescope, Turnitin, Otter.ai, Formative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eAI improves grading accuracy by 40%. [30-31, 34-35, 75, 98, 101, 103]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eFaculty Assistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAI-generated course materials.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAutomated lesson planning and recommendations.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAI-driven academic content suggestions.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eContent creation and syllabus planning.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eCanvas AI, Blackboard Learn, Google Classroom, Moodle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eAI enhances faculty efficiency by 35% [30-31, 34-35, 75, 98, 101, 103]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eEthical Concerns \u0026amp; Bias\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003ePotential biases in AI training datasets.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eRisks of misinformation and content biases.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eEthical AI fairness concerns.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eBias-mitigation strategies evolving.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eExplainable AI (XAI), Fairness Indicators, AI Ethics Toolkit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eEthical AI remains a key challenge in education \u0026nbsp;[2, 15, 21, 22, 28, 41, 51, 64, 65, 69, 77, 83,\u003c/p\u003e\n \u003cp\u003e89, 114, 115, 119, 122, 127-128, 135, 137,144-160]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eData Privacy \u0026amp; Security\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAI-driven data protection measures.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eRisks of data breaches and misuse.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eEnsures user privacy through encryption.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eAI-driven privacy protection mechanisms.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eGDPR-Compliant AI, Safe Exam Browser\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eAI privacy concerns impact adoption by 20% [13-14, 27-28]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eImpact on Traditional Teaching\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAugments but does not replace teachers.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAI-assisted blended learning approaches.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eAI-powered virtual teaching assistants.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eComplement educators rather than replacing them.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eTutorMe, Cognii, AI Teaching Assistants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eAI enhances rather than replaces educators [1, 8, 19, 29-32, 60, 61, 71, 123]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eSupport for Teaching Models\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eEffective for research-based learning.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eSuitable for flipped classrooms.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14px;\"\u003e\n \u003cp\u003eOptimized for blended and hybrid learning.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003eStrong multimodal learning support.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eGoogle Classroom, Microsoft Teams for Education, Moodle AI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eAI supports all modern teaching methodologies [1], [7, 10, 39, 85, 98, 105-106, 124, 138]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFurther, innovations in education, recognizing AI\u0026apos;s potential to transform the educational system and increasing efficiency, investigate the pedagogical implications of AI base tools in higher education [4-15, 79-85, 138]. AI-based Learning Tools (AI-LTs ) integration can increase productivity and revolutionize educational systems. The management, administration, student recruitment, and pedagogical processes in international higher education, online \u0026amp; remote learning, will improve sustainability and development [55-56]. To change the ways of learning interactions from conventional to machine-focused \u0026amp; knowledge-centered methodologies, focus on learners\u0026apos; needs and the role of AI-based Learning Tools (AI-LTs ) in the education system [70-71, 91-92].\u003c/p\u003e\n\u003cp\u003eSeveral studies investigate how AI-based tools, such as adaptive learning platforms and intelligent tutoring systems, lead to improving academic performance, increasing student motivation, and enhancing personal learning [4-15, 36-40, 44-48, 94, 110]. The results of [84-85] indicate the importance of AI-based Learning Tools (AI-LTs) enhancing learning outcomes, particularly in developing students\u0026rsquo; skills, fostering group work, and making a more effective research environment. Another point [120-123] brought out is that AI systems could be applied to online education to enable large-scale, individualized interactions between learners and instructors. According to studies [79-80], AI tutoring systems significantly improve student performance outcomes in higher education.\u003c/p\u003e\n\u003cp\u003eMoreover, the nature of higher education learning revolutionizes some fields, such as boosting access, retention, enhancing learning, improving instruction, assessment, feedback, trimming costs, time, and administration management [1-2, 4-15,19, 36-40, 94, 113,126-128].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 3: Details of key parameters in learning factors\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFactor/Parameter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDefinition \u0026amp; Impact\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStudies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003ePersonalized Learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAdaptive Learning Systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI tailors content based on student needs and progress.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e[3, 20, 40-60]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAI-Powered Tutoring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI provides real-time personalized guidance.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLearning Path Optimization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI predicts the best learning paths for students.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eStudent Engagement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eGamification \u0026amp; Interactive Learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI-driven engagement techniques boost retention.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e[40-60]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAI-Assisted Discussion Boards\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI moderates and enhances discussions.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eVirtual Reality (VR) \u0026amp; Augmented Reality (AR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI integrates VR/AR for immersive learning.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eAdaptive Learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI-Based Recommendations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePersonalized study materials based on learning style.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e[4, 16, 26, 43-47, 60, 84, 89, 110]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIntelligent Feedback Systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI generates automated feedback to students.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eReal-Time Performance Analytics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI tracks and reports student progress.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eCritical Thinking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI-Powered Research Tools\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI aids in deeper analytical learning.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e[1-20, 30-50, 70-90]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCognitive Skill Enhancement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI assists in problem-solving exercises.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAI-Supported Debate Training\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI fosters argumentation and logic-building skills.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eAssessment \u0026amp; Feedback\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI-Based Grading Systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI automates grading and analysis.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e[30-31, 34-35, 75, 98, 101-103]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eIntelligent Exam Proctoring\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI ensures exam security and fairness.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePersonalized Feedback Generation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI customizes feedback for students.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eFaculty Assistance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI-Assisted Curriculum Development\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI helps in designing course content.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e[30-31, 34-35, 75, 98, 101, 103]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAutomated Content Generation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI auto-generates quizzes, assignments, and study materials.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDigital Teaching Assistants\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI helps faculty manage classes efficiently.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003eEthical Concerns\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAlgorithmic Bias \u0026amp; Fairness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI biases in data affect learning outcomes.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\"\u003e\n \u003cp\u003e[2, 15, 21-28, 41\u003c/p\u003e\n \u003cp\u003e,51, 64 -69, 77, 83,\u003c/p\u003e\n \u003cp\u003e89, 114-122, 127- 137]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eData Privacy \u0026amp; Security Risks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI in education must ensure user privacy.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTransparency in AI Decisions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI\u0026rsquo;s role in grading must be explainable.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe study [3] indicates a favorable trend toward incorporating AI learning tools into learning environments, attributed to the technology\u0026apos;s recognition as cutting-edge teaching. According to [70-71], [91-92], AI teaching systems positively impact college students\u0026apos; environmental education.\u003c/p\u003e\n\u003cp\u003eTable 4: Metrics for Learning Outcome Assessment in AI-Based Higher Education.\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eMetric\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDefinition \u0026amp; Relevance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI\u0026rsquo;s Role in Enhancement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStudies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eKnowledge Retention\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMeasures how well students remember course material.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI-driven spaced repetition and adaptive learning.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[74]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eStudent Performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAnalyzes grades, test scores, and skill improvement.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI-based analytics track trends and weaknesses.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[25, 26]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eEngagement Rate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eTracks student participation in activities.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI monitors discussion forums and class interactions.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[16, 17]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePersonalized Learning Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMeasures how well AI adapts to student needs.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI-driven dynamic content recommendations.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[41, 66, 67]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eTime to Competency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAssesses how quickly a student master\u0026rsquo;s a topic.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI accelerates learning with focused materials.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[49, 50]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eCritical Thinking Score\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eEvaluates problem-solving and reasoning skills.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI fosters cognitive development via challenges.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[131]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eAssessment Accuracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eMeasures the reliability of grading and feedback.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI reduces grading errors and subjectivity.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[75]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDropout Prediction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eIdentifies students at risk of failing.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI analyzes data to suggest early interventions.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[41]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFurthermore, AI-based Learning Tools (AI-LTs ) can potentially transform social relationships in higher education, affecting students, instructors, and technology systems [42-48, 110]. The [42-48] studies provide evidence of the beneficial effects of an interactive chatbot on the motivation and engagement of engineering students. According to Essel et al. (2022) [63], students who interact with chatbots [42, 63] or virtual teaching assistants perform better academically. According to [42, 63], AI-based chatbots have a favorable effect on teamwork and learning outcomes.\u003c/p\u003e\n\u003cp\u003eRecent developments in virtual and immersive learning platforms provide new directions for AI-based education. Prasetya et al. (2023) [161] demonstrated the effectiveness of metaverse-based virtual laboratories in enhancing students\u0026rsquo; cognitive and practical skills. Muskhir et al. (2024) [ 162] offered a comprehensive bibliometric analysis of virtual reality applications in vocational education, while Samala et al. (2024) [163] explored global trends and future challenges in educational technology integration. These studies further support the growing role of emerging AI-powered tools in reshaping higher education. Sambo et al. (2025) [164] conducted a systematic review on barriers to gamification adoption in education, identifying cultural, technological, and institutional challenges that hinder the effective implementation of educational innovations in developing countries. Their findings are relevant for understanding potential systemic obstacles in AI-LTs deployment, particularly in resource-constrained settings\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1 Analytics for learning and assistance for students.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe fields of AI-based Learning Tools (AI-LTs) have support in analytics and student innovation in education. Using student data and analytics to improve learning outcomes and educational experience has become more popular [18, 70-71, 79, 85, 91-92, 138]. AI-based learning technologies offer benefits in identifying at-risk students, suggesting tailored interventions, and enabling prompt feedback and assessment by enabling real-time analysis of massive amounts of data related to students\u0026apos; emotions as well as their learning [132-133, 139-142,144-160]. AI-driven learning analytics and student support systems can improve student progress and give educators valuable insights [109-111]. According to [52-54], machine-learning approaches successfully identify contextual characteristics that differentiate high-achieving and low-achieving pupils in reading proficiency. In a higher education context, [91-94] AI-based genetic algorithm grouping method performed better than traditional grouping methods. Ouyang et al. [109-111] explored how the groups collaborated within online interaction environments using AI algorithms and learning analytics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Evaluation and grading.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnother interesting area of exploration is the ability of AI-based Learning Tools (AI-LTs) to automate grading and assessment processes. Researchers study the potential benefits and drawbacks of automated grading, as well as the validity and reliability of AI-based grading systems when compared with traditional human grading methods [29-35, 97-105]. Natural language processing (NLP) and plagiarism detection are two examples of AI in assessment that can automate grading, lessen burden, and facilitate data-driven decision-making [29-35, 97-105].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 The professional development of educators.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAI-based Learning Tools (AI-LTs) support innovative pedagogy in teaching methodologies and frameworks as shown in Figure 2 , described the details of AI-integrated framework in higher education system. \u0026nbsp;Figure 2 provides a comparative analysis between traditional education models and AI-supported approaches, highlighting key shifts such as data-driven personalization, real-time feedback, intelligent content delivery, and adaptive learner support. This comparison underscores how AI integration enhances instructional flexibility, learning efficiency, and educational outcomes within modern higher education systems.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis research focuses on how AI technologies can support the development of teachers\u0026apos; positive attitudes and perceptions, flexible teaching methodologies, and personal learning experiences. It also covers the impact of AI on teachers\u0026apos; professional development [4-15, 29-35]. As a result of collaboration between institutions, legislators, and AI developers to address the integration problems about educational aid and the kind of support educators need in integrating AI technologies into their methods of teaching [4-15]. Interdisciplinary collaborations are required for comprehensive research and development on morality in AI-driven learning tools [52-54, 92-94, 135]. In AI-based learning environments, researchers focus on algorithmic bias, discrimination, justice, transparency, and accountability [126-128]. Figure 3 illustrates a focused framework for the integration of AI-based learning tools (AI-LTs) in higher education, emphasizing three core domains: administration, teaching, and learning. Within administration, AI supports data-driven decision-making, resource optimization, and predictive analytics. In the teaching domain, it enhances instructional design, automates content delivery, and provides real-time feedback. For learning, AI enables personalized learning paths, adaptive assessments, and continuous learner engagement. Together, these components demonstrate how AI-LTs can holistically enhance the operational and academic dimensions of higher education institutions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Ethical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical considerations in deploying AI technologies, including ensuring equity and inclusivity, and striking a balance between human teachers and AI tools, are considered. The impact of AI on society must be examined in detail, including changes in employment trends and the transformation of the workforce [19, 33-35, 95-96] and privacy violations, agency, responsibility, and surveillance [123]. Moreover, to address ethical concerns such as algorithmic bias, privacy violations, and the erosion of human-centric teaching, institutions should develop and implement clear ethical guidelines for AI integration. This includes transparent algorithmic decision-making, student consent protocols for data usage, and faculty training programs that emphasize human-AI collaboration rather than substitution. Institutional policies must prioritize fairness, inclusivity, and accountability to ensure responsible AI adoption in educational environments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.5 \u0026nbsp;Research Questions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study finds the answers to the following questions.\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eHow do AI-based Learning Tools (AI-LTs) affect learning in higher education?\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eWhat is the role of AI-based Learning Tools (AI-LTs) in higher education for students? \u0026nbsp;\u003c/li\u003e\n \u003cli\u003eHow do AI-based Learning Tools (AI-LTs) impact higher education students, faculty, and researchers?\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"3.\tMethodology","content":"\u003cp\u003eThis critical review employs a systematic approach to evaluating existing research to understand the impacts and find the challenges of AI-based Learning Tools (AI-LTs), like DeepSeek, GPT_4, Gemini, etc.\u0026nbsp;The methodology is structured to ensure a comprehensive and unbiased investigation of the relevant literature, addressing the strengths and limitations of current findings. This study has adopted a qualitative approach for collecting and analyzing electronic databases to identify peer-reviewed articles published in English since 2020. Given the diversity of methodologies, reporting styles, and outcome variables across the included studies, a quantitative meta-analysis was not feasible in this review. The improvement percentages cited (e.g., 30\u0026ndash;40% better learning outcomes) are extracted from individual studies and are not statistically calculated within this paper. Future research should focus on quantitative synthesis and meta-analysis to provide robust statistical confirmation of AI-LTs impacts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.1 Research Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research follows a \u003cstrong\u003equalitative, exploratory design\u003c/strong\u003e using a systematic literature review method. This design is suited to synthesize existing studies, identify thematic patterns, and evaluate trends related to AI-LTs such as ChatGPT, DeepSeek, Meta AI, and Gemini within higher education. The review aims to answer three guiding questions:\u003c/p\u003e\n\u003col start=\"1\" type=\"I\"\u003e\n \u003cli\u003eWhat is the impact of AI-LTs on student learning outcomes?\u003c/li\u003e\n \u003cli\u003eHow do faculty utilize AI-LTs for instructional enhancement?\u003c/li\u003e\n \u003cli\u003eWhat ethical and societal challenges arise from the adoption of AI in academia?\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Study Selection and Data Extraction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study adopts a qualitative, exploratory research design grounded in a systematic literature review approach. The goal is to synthesize current trends, impacts, and ethical challenges associated with AI-based learning tools (AI-LTs) in higher education. The methodology was carefully structured to ensure transparency, rigor, and reproducibility, aligning with best practices in evidence-based educational research.\u003c/p\u003e\n\u003cp\u003eThe research design was guided by three primary objectives: first, to explore how AI-based tutoring systems and adaptive learning platforms influence student learning outcomes; second, to investigate how faculty utilize AI-LTs for enhancing instructional practices; and third, to critically examine the ethical and societal challenges that accompany the integration of AI in academia. This design was appropriate given the emergent nature of AI technologies and their multifaceted role across educational systems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Search Strategy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA comprehensive literature search was conducted using major academic databases including Scopus, Web of Science, Google Scholar, ERIC, SpringerLink, and IEEE Xplore. The search employed relevant keywords and phrases such as \u0026ldquo;AI in education,\u0026rdquo; \u0026ldquo;AI-based learning tools,\u0026rdquo; \u0026ldquo;ChatGPT in higher education,\u0026rdquo; \u0026ldquo;DeepSeek,\u0026rdquo; \u0026ldquo;adaptive learning,\u0026rdquo; \u0026ldquo;AI tutoring systems,\u0026rdquo; \u0026ldquo;AI ethics in education,\u0026rdquo; \u0026ldquo;Meta AI,\u0026rdquo; \u0026ldquo;Gemini AI,\u0026rdquo; and \u0026ldquo;LLMs in teaching.\u0026rdquo; Boolean operators were used to combine terms where appropriate. The search was limited to peer-reviewed sources published between January 2020 and May 2024 to ensure that the analysis reflects recent advancements and current educational practices influenced by AI.\u003c/p\u003e\n\u003cp\u003eClear inclusion and exclusion criteria were defined to guide the selection of studies. Included studies were those that focused specifically on the application of AI tools in higher education contexts, were published in peer-reviewed journals or conference proceedings, and addressed themes such as pedagogical impact, learning outcomes, or ethical dimensions. Studies were excluded if they centered solely on primary or secondary education, were non-English publications, or lacked full-text access or methodological transparency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Study Selection Process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study selection process followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The review process involved four stages: identification, screening, eligibility, and inclusion. Initially, 312 records were retrieved. After title and abstract screening, followed by full-text eligibility review, a total of 92 studies were selected for final inclusion. The flow of this process is illustrated in the PRISMA diagram provided in Figure X.\u003c/p\u003e\n\u003cp\u003eTo ensure systematic and consistent data collection, a structured data extraction framework was applied. For each included study, relevant details were recorded such as the study title, year of publication, the specific AI tool analyzed, the educational setting, reported outcomes (e.g., engagement or personalization), ethical concerns (if mentioned), and the platform or model (e.g., ChatGPT, DeepSeek, Gemini) discussed. Data extraction was carried out independently by two researchers, and any disagreements were resolved through discussion with a third reviewer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Quality Assessment (SALSA Framework)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe methodological quality and relevance of each selected study were assessed using the SALSA framework, which comprises four phases: Search, Appraisal, Synthesis, and Analysis. The search strategy was methodologically robust, while appraisal involved evaluating each study based on relevance, clarity, and risk of bias. Synthesis involved identifying themes and patterns across studies, and analysis focused on drawing meaningful conclusions aligned with the objectives of this review.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Data Synthesis Approach\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data synthesis approach relied on thematic analysis, where recurring concepts and themes were categorized and analyzed across the selected studies. Where studies reported quantitative improvements, such as \u0026ldquo;30\u0026ndash;40% better personalized learning outcomes,\u0026rdquo; these values were directly extracted from the original sources. It is important to note that such percentages are not the result of meta-analytical averaging but are based on self-reported or empirically observed outcomes in individual studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7 Ethical Considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical considerations were carefully incorporated into the review process, even though this study relies on secondary data. The analysis identified key ethical concerns associated with AI deployment in higher education, including algorithmic bias, data privacy, surveillance risks, and issues of equity and access. Additionally, the review highlights how AI may alter traditional teacher-student dynamics, necessitating professional development programs to help educators and learners adapt responsibly. A dedicated subsection has been included in the discussion to consolidate and reflect on these ethical challenges.\u003c/p\u003e\n\u003cp\u003eIn the methodological framework, we \u0026nbsp;ensure both breadth and depth in understanding the multifaceted impact of AI-LTs in higher education. It balances technical rigor with ethical sensitivity, offering a foundation for robust analysis and meaningful academic insights.\u003c/p\u003e\n\u003cp\u003eThe SAMR model [164] classifies technology use in education into four levels: Substitution (technology acts as a direct tool substitute with no functional change), Augmentation (technology acts as a substitute with functional improvements), Modification (technology allows significant task redesign), and Redefinition (technology enables previously inconceivable tasks). AI-based Learning Tools like adaptive tutoring systems typically fall between the Modification and Redefinition levels, as they fundamentally reshape learning processes and offer capabilities beyond traditional teaching methods\u003c/p\u003e\n\u003cp\u003eIn this study we adopted the SAMR model (Figure 4) (Substitution, Augmentation, Modification, Redefinition) to evaluate the integration of AI-based Learning Tools (AI-LTs) in higher education. This model helps classify AI-LTs applications based on their depth of technological integration and educational impact, offering a systematic framework to assess their role in transforming teaching and learning processes.\u003c/p\u003e\n\u003cp\u003eThe selection of related published papers, following the initial search, involved two reviewers screened the titles and abstracts of articles independently to determine their relevance to the inclusion and exclusion criteria. It was also considered that disagreements were resolved through discussion and, if necessary, consultation with a third reviewer. The selected studies and full-text articles were retrieved. This review narrows its primary focus to AI-based tutoring systems\u003cem\u003e\u0026nbsp;a\u003c/em\u003egainst the inclusion and exclusion criteria. A standardized data extraction format was used to collect information on the respective study as per study characteristics (like author, year, country), sample characteristics (like sample size, age range), and key findings related to the role of AI-based Learning Tools (AI-LTs) in higher education. Two reviewers extracted data, and discrepancies were justified for their selection by discussing independently.\u003c/p\u003e"},{"header":"4.\tResults \u0026 Discussion \u0026 Findings of the review study","content":"\u003cp\u003eIn this section, we discuss the findings of the study according to research questions in sequence:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1 \u0026nbsp;AI-based Learning Tools\u0026rsquo; (AI-LTs) effect on learning in higher education\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ei. Growth of AI Research in Higher Education\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe number of studies on AI tools in higher education have grown significantly in recent years [4-15], [23-24], [36-40]. Studies [36-40] analyzed the top 50 AI-related studies in higher education, focusing on predicting important student outcomes like academic achievement, student retention, and dropout rates. However, these studies often overlooked key areas like collaboration, communication, self-efficacy, and higher-order thinking abilities. These aspects are essential for student success and were widely ignored in the research, suggesting the potential gap in the current AI-based Learning Tools (AI-LTs) role in higher education.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eii. Innovations in Teaching and Learning\u003c/strong\u003e\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eAI Tools for Learning Enhancement:\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThe learning methodologies, like adaptive learning platforms, intelligent tutoring systems (ITS) or AI-based Learning Tools (AI-LTs), have great potential to change personalized learning and improve academic achievements in higher education. According to [4-15], these AI-based technologies rely on algorithms that analyze students\u0026apos; learning patterns and instructional content so these can be updated in real-time. This approach helps learners receive tailored resources and support to grade their progress and create a more effective learning environment. Personalizing content delivery allows adaptive learning systems to bridge understanding gaps among learners, especially students with diverse needs or those who may fail in the regular classroom environment.\u003c/p\u003e\n\u003cp\u003eAccording to [36-40], [94], AI learning tools promote learning experience through personalized feedback. The interactive feedback loop ensures students do not fall behind, as they receive support precisely when needed. ITS-AI-based systems can also trace long-term academic trends and support instructors with data to improve teaching strategies.\u003c/p\u003e\n\u003cp\u003eAccording to [79-81], AI tutoring systems improve student performance, develop confidence and allow them to work at their own pace. Learners who used AI-driven tutoring platforms performed better than their peers in traditional systems. These systems, designed to offer personalized feedback and adapt to individual student needs, ensure that learners receive consistent support, especially in areas where they struggle.\u0026nbsp;\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003eOnline Learning:\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAccording to [120-123], AI-based Learning Tools (AI-LTs) have transformative capabilities on a large scale among learners. Further, the authors presented how AI-LTs are used in managing and analyzing vast amounts of data related to students in developing a personalized learning mechanism. These systems allow instructors to handle larger classes but still allow for a high degree of personalization and are, thus, an essential tool for developing online learning environments. According to [91-92], AI-LTs contribute to fostering knowledge-centered approaches in education, significantly enhancing students\u0026apos; interest in learning. Further, more dynamic and interactive learning environments can be created using AI-LTs, active participation, and deeper learning. This methodology supports knowledge-centered teaching, which aims for deep engagement with subjects and encourages the critical exploration of concepts rather than the passive intake of information. Therefore, AI-LTs help create a student-centered environment in which students are motivated to interact with the subject problems to understand questions better.\u0026nbsp;\u003c/p\u003e\n\u003cul type=\"disc\"\u003e\n \u003cli\u003e\u003cstrong\u003ePractical Outcomes:\u003c/strong\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eAccording to [55-56], AI-based system learning tools are vital in enhancing administrative efficiency and sustainability in higher education systems globally. They found it time and effort saving, like enrollment, queries about students, and scheduling. AI-driven systems can automate these functions, ensuring faster and more accurate data volumes, a primary requirement for institutions. In the international education system, these tools will overcome the complexities of regulations, logistics, and communication problems.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese AI-LTs help institutions manage resources more effectively. Data-driven insights from AI systems also enable informed decisions about students\u0026apos; needs, trends, and outcomes. A data-centric approach supports long-term sustainability in the AI-based education system, allowing institutions to evolve in line with the dynamic changes occurring around the globe while still maintaining the highest standards of education and streamlining operations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eiii. Learning Analytics and Student Support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea. AI-driven learning analytics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to [139-142], using AI-driven learning analytics provides real-time feedback on both emotional states and learning progress, an essential development in the learning, understanding, and support of students in a broader way. Further, the study discusses that AI-based learning systems, from facial expressions to body language and even tone of voice, might analyze excess data points to effectively determine the learner\u0026apos;s emotional state amid learning activities. The AI-driven system provides immediate feedback on academic and psychological well-being that allows the detection of stress or disengagement by giving AI-based stimuli to make a personalized intervention to improve the quality of education. This methodology of real-time emotional feedback helps create a more responsive learning environment, allowing educators to address emotional barriers to learning and provide timely support before minor issues become significant obstacles to academic success. Moreover, learning analytics based on AI is pivotal in offering evidence-based interventions tailored to at-risk students; research has proven that this individualized intervention helps improve retention rates and empowers students.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb. AI-based Learning Tools (AI-LTs) - collaborative efforts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the studies [91-94] emphasized that the AI-based grouping methods significantly outperform traditional methods (random selection or instructor-based grouping) in collaborative learning approaches. Students working in AI-constructed groups were more communicative, completed more tasks, and shared the workload more equitably. These results indicate that AI-based learning can improve the quality of group work by facilitating better collaboration, deeper engagement, and more optimal learning outcomes.\u003c/p\u003e\n\u003cp\u003eFurther studies [109-111] extend an idea of AI-enhanced collaboration by emphasizing its effect on online group interactions. The research shows that AI-driven tools improve collaborative group outcomes in virtual learning environments more effectively in communication, coordination, and problem-solving phases. AI-based learning systems can monitor online discussions and interactions, providing feedback or nudges to encourage active participation and encourage group members to stay on task prompts to stimulate further discussion and helping teachers intervene at the appropriate moment of need.\u003c/p\u003e\n\u003ch3\u003e4.2 Findings on the Moral and Societal Ramifications of AI in Higher Education\u003c/h3\u003e\n\u003ch3\u003e\u003cstrong\u003ei. Pedagogical Implications and Innovations\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003eAI-powered tools, such as DeepSeek, GPT, and Gemini for adaptive learning and intelligent tutoring systems, improve learning outcomes by personalizing education and fostering student engagement [44-48, 94, 110]. AI-based learning systems can adapt the latest tools enhanced to learners\u0026apos; needs, improve academic results, and encourage collaboration [79-81]. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eii. Learning Analytics and Student Support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe AI-based Learning Tools (AI-LTs) and analytics identify the students who are likely to fail and offer interventions at the right time [132-133, 139-142]. Studies [52-54] used machine learning methodologies to predict contextual factors affecting academic performance with a high success ratio.\u0026nbsp;\u003c/p\u003e\n\u003ch4\u003eTable 5 :The Role of AI in Learning: Higher Education Findings in Literature\u003c/h4\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003eStudies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eFocus Area\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eKey Findings\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e[36-39]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eTop AI studies in higher education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eForecasting student outcomes: lack of focus on collaboration and communication.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e[79-81]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eAI tutoring systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eSignificant improvements in student performance.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e[91-94]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eAI-based grouping for collaboration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eImproved group performance compared to traditional methods.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e[52-54,94]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eMachine learning for reading proficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eIdentification of contextual factors for student success.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e[59-62]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eMachine learning for teacher feedback analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eImproved teaching strategies based on student feedback.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17px;\"\u003e\n \u003cp\u003e[120-123]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32px;\"\u003e\n \u003cp\u003eAI in online learning\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003eLarge-scale personalization and ethical concerns like surveillance.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eiii. Educator Development and AI Integration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAI-based tools, including DeepSeek, GPT, Gemini, etc., assist teachers in analyzing feedback and adapting teaching methods [59-62]. Institutions must address educators\u0026apos; concerns about AI integration and provide proper training [4-15]. The introduction of AI tools changed traditional teaching roles and created a need for balanced approaches to retain human agency and creativity in teaching [20].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eiv. Automation of Evaluation and Grading\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAI-based tools, such as NLP-oriented AI-based Learning Tools (AI-LTs ) and automated grading systems, reduce teachers\u0026apos; workloads, ensure speedy feedback, and make grading much easier [29-32, 97-101]. While AI-based grading systems enhance efficiency, concerns remain about their reliability compared to human grading [33-35].\u003c/p\u003e\n\u003ch4\u003e\u003cstrong\u003ev. Societal Impact and Workforce Implications\u003c/strong\u003e\u003c/h4\u003e\n\u003cp\u003eIncorporating AI into education and related fields influences job roles, necessitating reskilling and adaptation in the workforce [95-96,120-123]. The increased reliance on AI reshapes interactions between students, educators, and AI systems, raising questions about the balance between technology and human connection [4-15].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003evi. AI Literacy and Awareness\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to [86-88], the need to define AI literacy and create accessible materials for non-experts to better understand the limitations in the education field is essential. Interdisciplinary research is also essential to address ethical concerns, improve transparency, and develop responsible AI practices [20].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 6 :Key Facts of Learning and Findings\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFocus Area\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKey Findings\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e\u003cstrong\u003estudies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAI in Learning Analytics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eReal-time data analysis identifies at-risk students, enabling tailored interventions.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e[139-142]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStudent Performance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eAI tutoring systems boost overall academic performance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e[79-81]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEthical Concerns\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eAlgorithmic bias and privacy risks demand ethical frameworks\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e[120-123, 126-128]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCollaboration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eAI-based grouping methods enhance team effectiveness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e[91-94]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEfficiency in Assessment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eAI automates grading, reducing educator workload\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e[29-35]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducator Support\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eAI aids in professional development by personalizing teaching strategies\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e[59-62]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWorkforce Transformation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eAI-driven changes in education impact job roles and reskilling requirements\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 24px;\"\u003e\n \u003cp\u003e[95-96]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThese findings underscore AI\u0026apos;s potential to revolutionize higher education while highlighting ethical, societal, and pedagogical challenges that demand careful consideration and collaboration across disciplines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCase study\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies [52-54], [94] presented how machine learning algorithms can effectively differentiate between high- and low-achieving reading proficiency, with valuable insights to be drawn toward student support. This proactive methodology enables educators to implement target interventions, such as additional resources for low-achieving students and further challenges to high-achieving students. The use of machine learning keeps on scoring and following trends in students\u0026apos; reading performance over a period, allowing detection of trends and hints at early signs of academic struggle. The approach reflects growing AI and machine learning roles transforming educational practices as they facilitate even more targeted and evident-based interventions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eiv. Automated Assessment and Grading\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to [29-32], [97-101] studies, AI tools, especially Natural Language Processing (NLP) applications, have excellent benefits in grade automation and assessment efficiency. They reveal that AI-based learning applications in grading enable instructors to minimize their efforts while increasing accuracy and reliability during evaluation. NLP-AI-based applications can accurately grade written content through OCR methods, such as essays, reports, and open-ended responses. The NPL-based tools provide accurate support by analyzing various linguistic features, including grammar, syntax, and coherence. These tools reduce human bias, ensure standardized grading, and save instructor\u0026rsquo;s valuable time.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to [33-35] studies, AI helps in plagiarism detection and data-driven decision-making, giving some insight into rewriting the book on academic integrity and educational management. AI-powered plagiarism detection tools are becoming increasingly sophisticated as they use machine learning (ML) algorithms to detect subtle forms of content duplication and citation issues. There is a significant reduction in the likelihood that cheating will remain hidden, allowing academics to ensure proper standards. Furthermore, AI enables the aggregation and analysis of student performance data in new ways. It also finds patterns among students\u0026apos; behaviors, engagement levels, and academic performance, offer insights for educators and administrators to implement in curriculum designs, learning methods, and institutional policies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ev. Educator\u0026rsquo;s Professional Development-Flipped \u0026amp; Blended Learning\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe studies [59-62] suggest that AI-based Learning Tools (AI-LTs) will make teachers\u0026apos; professional development much easier; it may facilitate flexible forms of teaching that diversify resources in comparison with conventional methodologies to teachers. AI can scrutinize the interaction within a class, results in learning and students\u0026apos; interests, and offers more suitable, person-specific teaching models and materials for diverse classroom environments. In this way of AI-based learning, the teacher has enough time to implement flipped classroom models or blended learning, and this is all done by giving a lot of administrative and grading time back to teachers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAccording to [4-15] studies, an effective AI integration into educator professional development depends on collaboration between educational institutions, policymakers, and AI developers. As AI-based research continues to reshape teaching and learning environments, educators must be trained to use AI tools in their classrooms meaningfully. Therefore, the study emphasized collaboration in developing frameworks for AI training programs so that teachers are equally acquainted with the technical aspects of AI to enhance learning outcomes.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003evi. Social and Ethical Implications\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAccording to [4-15, 126-128] studies, several significant challenges in integrating AI technologies into education include algorithmic bias, discrimination, and privacy violations\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStudies [120-123] deal with the ethical dilemmas that arise to balancing human and AI roles in education. Though it may enhance educational practices, AI poses a looming threat to the diminishing role of human educators. Furthermore, studies [18, 135-137] suggest solutions to these challenges by promoting human-centered AI-based application values and fostering interdisciplinary collaboration. According to a study [113], AI-based transformative impacts on the labor force and changes in employment trends. The paradigm shift toward more technology-driven roles will demand rethinking skills development and educational curricula, respectively. \u0026nbsp; According to studies [36-40], researchers discuss investments in education, retraining programs, and policies that encourage the development of AI systems to mitigate the adverse effects of workforce disruption which may displace some jobs, providing opportunities for more creative, strategic, and interdisciplinary work to reshape the economy, as detailed in Table 5.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe increasing usage of AI-based tools in academia and industry critically focuses on concerns regarding systems\u0026apos; fairness, discrimination, and transparency [118-119, 126-128], the details shown in Table 6. The researchers recommend ethical frameworks to mitigate algorithmic bias, exclusion, and inequality in AI-based applications for learning outcomes in the higher education system [4-15, 135]. \u0026nbsp;According to [120-123] studies, there is a potential threat of privacy invasion globally, and the need for appropriate ethical control of AI systems. Moreover, Dignum (2017) [51] reveals the significance of \u0026nbsp; AI implementation with cultural norms, human-centered values, and ethical reasoning in AI-based systems.\u003c/p\u003e\n\u003ch3\u003e4.3 Future of AI-based tools like DeepSeek, GPT, Gemini etc. in Higher Education \u0026nbsp;\u003c/h3\u003e\n\u003cp\u003eThe AI forecasts students\u0026apos; learning status, including academic achievement, retention rates, and dropout prediction [36-40, 94] shown in Table 7. \u0026nbsp;The AI systems help to define AI-based literacy and create accessible materials for non-experts to foster an understanding of all teaching methodologies [86-88]. AI-based learning systems enable real-time analysis of student data, identifying at-risk students and offering significant feedback [109-111, 139-142].\u003c/p\u003e\n\u003cp\u003eThe AI-powered adaptive learning platforms and tutoring systems improve academic outcomes [44-48, 110]. Technology like AI tutoring systems and chatbots provides personalized support, boosting engagement and performance [42, 63]. AI-based learning systems promote group projects, create inclusive learning environments, and facilitate teamwork through tools like genetic algorithm-based grouping [91-94]. AI-based systems improve higher education institutions\u0026apos; management, administration, and recruitment processes [55-56]. Advanced systems, like AI-based Learning Tools (AI-LTs), automate grading, assessments, and plagiarism detection, reducing administrative burdens, increasing efficiency, and ensuring reliable evaluations [97-101, 103-105]. AI-based systems enhance professional growth by analyzing feedback and providing targeted training and resources. For instance, machine learning approaches like \u0026quot;Social Mining\u0026quot; help refine teaching methods based on student feedback [59-62]. Collaboration among institutions, policymakers, and developers is vital to overcome challenges and support educators in adopting AI tools [4-15].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 7 :Detailed key factors of learning and findings\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eKey Areas of Learning\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eFindings/Benefits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eStudies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eLearning Analytics\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eReal-time student data analysis, identifying at-risk students, tailored feedback.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[109-111, 139-142]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003ePedagogical Innovation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eImproved learning outcomes, engagement, and personalized learning.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[4-15, 44-48, 110]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eAdministrative Efficiency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eCost and time savings through automation in grading, assessments, and management.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[97-101, 103-105]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEthical Concerns\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBias, discrimination, privacy, and the need for accountability in AI systems.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[120-123, 126-128]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003e\u003cstrong\u003eEducator Development\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eAI-supported training and development, feedback-based improvement of teaching methods.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e[4-15, 59-62]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eThe findings reveal that AI-based Learning Tools (AI-LTs) are transforming the higher education system by introducing innovative teaching and learning hybrid approaches. This imbalance suggests that future studies should expand their focus on the growth of the educational field. AI-powered adaptive learning platforms and intelligent tutoring systems have remarkably succeeded in improving personalized learning and student engagement.\u003c/p\u003e\n\u003cp\u003eFurthermore, AI-based systems\u0026apos; role in online learning and administrative efficiency enhances the scalability and sustainability of the technologies. Some Tools equipped with automated language-generating features, like NLP, have streamlined assessment processes [29-32, 97-101]. Despite the growing potential of AI-based learning tools (AI-LTs), their deployment in higher education raises significant ethical concerns. As shown in Figure 5, key challenges include algorithmic bias, surveillance-related risks, unequal access, data privacy issues, and the evolving dynamics between teachers and students. These concerns question the validity, reliability, and fairness of AI systems compared to traditional human evaluators. The figure highlights the urgent need for structured professional development programs that equip both educators and learners with the skills and awareness necessary to responsibly integrate AI-LTs into academic environments. This emphasizes the need for structured professional development programs to equip teachers and students with AI-based Learning Tools (AI-LTs) and integrated skills.\u003c/p\u003e\n\u003cp\u003eAI-based tools present transformative opportunities and ethical challenges shown in Figure 5, such as algorithmic bias, discrimination, and privacy concerns. They require balancing the human and AI roles in the education field. Our study focuses on the need for human-centered AI and interdisciplinary collaboration to ensure inclusivity, transparency, ethical implications, and accountability of advanced learning tools.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe findings of this study underscore the transformative potential of AI in higher education, particularly in enhancing personalized learning experiences, improving administrative efficiency, and fostering collaboration among students and educators. AI technologies can revolutionize instructional strategies, provide immediate feedback, and customize learning pathways to meet individual student needs. However, the study also highlights AI\u0026apos;s ethical and societal challenges, including algorithmic bias, privacy concerns, and the potential dehumanization of education. This study emphasizes the importance of AI-based literacy and professional development for educators to effectively integrate AI-based Learning Tools (AI-LTs) such as DeepSeek, GPT, Gemini, Meta AI, etc., into their learning methodologies for higher education practices.\u003c/p\u003e"},{"header":"5.\tLimitations","content":"\u003cp\u003eIn the study, while we provide valuable insights into the role of AI-based tools in higher education but it has several limitations. This study is a systematic literature review relying solely on secondary data from peer-reviewed articles. The absence of primary empirical data is a key limitation, which may reduce the practical validation and originality of the findings. Future studies should incorporate empirical methodologies such as surveys, interviews, or case studies to strengthen evidence-based conclusions and validate the impact of AI-based Learning Tools (AI-LTs) in real-world higher education contexts. The selected sample size is limited to a specific number and broader domain of learning, and the findings may not be generalizable to all higher education institutions globally and specific aspects of learning. Additionally, the study relies on self-reported data, which may introduce biases based on participants\u0026apos; perceptions and experiences. The study also focuses primarily on the perspectives of faculty, administrators, and students without considering the views of AI developers or technology companies involved in AI tool creation. Future research could address these limitations by expanding the sample size and exploring the perspectives of other key stakeholders involved in AI implementation in higher educational institutions.\u003c/p\u003e"},{"header":"6.\tConclusion","content":"\u003cp\u003eThis survey explored and discussed the deep-dive insights of the current AI-based Learning Tools (AI-LTs) and their impact on future trends. AI-based innovations hold immense potential to revolutionize higher education by improving learning, collaboration, and administrative processes. We analyzed the literature through existing and relevant studies to verify the impacts of these tools on higher education systems. However, the ethical, social, and pedagogical challenges associated with its integration are essential to developing higher educational systems. Educators, policymakers, and researchers must collaborate to ensure the deployment of such tools while fostering an inclusive and equitable educational landscape. This research survey refines AI-based applications and explores gaps, focusing on how the technology\u0026apos;s transformational power of AI benefits all stakeholders in higher education.\u003c/p\u003e"},{"header":"7.\tFuture Directions","content":"\u003cp\u003eThe findings suggest expanding AI applications to develop critical thinking, communication, and self-efficacy skills. Integrating AI-based Learning Tools (AI-LTs) requires technological advancements and a commitment to ethical and cultural alignment for implementations (Figure 6). Figure 6 illustrates the envisioned future of AI-based learning tools (AI-LTs) within global academia, highlighting their potential to cultivate critical thinking, communication, and learner self-efficacy. The model emphasizes the need for sustained technological innovation alongside ethical, cultural, and institutional alignment to ensure responsible and inclusive implementation. It also points toward emerging research frontiers involving AI developers, industry partnerships, and broader societal impacts\u0026mdash;areas that warrant deeper exploration in future studies.The study may explore other remaining parameters, such as developers, industry, and social impacts, which will also be considered in future work studies.\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnwar Ali Sathio conceptualized the study, led the literature review, and drafted the initial manuscript. \u0026nbsp;Prof. Dr. Muhammad Malook Rind provided critical supervision, methodological guidance, and revisions to enhance scholarly rigor. Mehboob Ali contributed to data analysis, interpretation, and manuscript editing. Ghulam Ahmed supported the design of the theoretical framework and contributed to academic validation. Sameer Ali assisted in data curation, formatting, reference management, funding, and acquisitions of resources.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval and Consent to Participate:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Statement:\u0026nbsp;\u003c/strong\u003eThis research received no specific grant from any funding agency in the public, commercial, or non-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials:\u0026nbsp;\u003c/strong\u003eNot applicable. The study is based on secondary literature and does not generate new datasets.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Registration:\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board (IRB) Approval:\u003c/strong\u003eNot applicable, as the study does not involve human or animal participants.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdous, M. (2023). \u003cem\u003eAI and the Future of Higher Education: Transforming Learning, Teaching, and Administration\u003c/em\u003e. Springer.\u003c/li\u003e\n\u003cli\u003eAbdous, M. (2023). \u003cem\u003eEthical considerations in AI-driven education: Challenges and solutions\u003c/em\u003e. Journal of Educational Technology, 15(3), 45-61.\u003c/li\u003e\n\u003cli\u003eAlmaiah, M. A., Al-Khasawneh, A. L., \u0026amp; Althunibat, A. (2022). 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SN Computer Science, 5(8), Article 1175. https://doi.org/10.1007/s42979-024-03538-1\u003c/li\u003e\n\u003cli\u003eRomrell, D., Kidder, L. C., \u0026amp; Wood, E. (2014). The SAMR model as a framework for evaluating mLearning. \u003cem\u003eJournal of Asynchronous Learning Networks\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(2), n2.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"AI-LTs, Higher Education, Teaching, Learning, Adaptive Platforms, Ethical Challenges","lastPublishedDoi":"10.21203/rs.3.rs-6460706/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6460706/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Artificial Intelligence based Learning Tools (AI-LTs) are rapidly transforming higher education by enhancing teaching, learning, and administration. This study systematically reviews peer-reviewed literature from 2020 to 2024 to explore the roles, benefits, and challenges of AI-LTs such as DeepSeek, ChatGPT, Meta AI, and Gemini. Using a qualitative methodology, we identified and analyzed studies from Scopus and Web of Science databases, applying rigorous selection and data extraction criteria. Findings reveal that AI-LTs significantly improve personalized learning, student engagement, and administrative efficiency but also raise ethical concerns including algorithmic bias and data privacy risks. The study emphasizes the need for responsible AI integration through faculty training, transparent algorithms, and human-AI collaboration. Future research should focus on primary empirical studies to validate AI-LTs impacts in diverse educational contexts.","manuscriptTitle":"Cognitive Metamorphosis in Higher Education: AI-LTs Nexus of DeepSeek, GPT, and the Future of Scholarly Engagement","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-03 08:22:14","doi":"10.21203/rs.3.rs-6460706/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"06d5dd86-b5c9-4cb8-85c4-e555e0733962","owner":[],"postedDate":"July 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-24T13:54:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-03 08:22:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6460706","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6460706","identity":"rs-6460706","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}