There is a cosmic showdown concocting to decipher which a nation will oversee future of wide-ranging ken like education since artificial intelligence (AI) is behooving ubiquitously deep-seated in culture. AI technologies have arête to alchemize educational practices by feeding individualized insight gained, robotizing authoritative tasks, and contributing avant-garde database for decisiveness. Technology boosts students learn anytime appositely that tailors their cognitive approach (Srivastava et al., 2023)¹. Moreover, AI passably embellishes appraisal modes for improved prospective career, implicating that higher education institutions (HEI) should embrace further AI to build expertise to suffice future market. Despite these bright blessings, AI into higher education systems upheaves multiple perturbations. Privity and aegis apprehensions, incredulity, cost and potential stigma are big deals that need to be addressed. However, leveraging AI can synthesize education more ubiquitous and panoramic enfranchising every learner to drive superior breeding. Thus, with becoming notoriety of AI and with its obligation to metamorphose education, demystifying relevance of AI in higher education is tectonic. Ergo, this research look-sees pertinence of AI in higher learning coupled with pros and cons as also preventions withstood through engineering it in pedagogic.

OBJECTIVE:

The paper purports to substantiate opportunities, challenges and preventions of AI in higher education.

METHODOLOGY:

The exploratory research in essence is carried out under manifold abstracts. Exploratory research is favored due to progressing more depth insight. Thus, the research essentially follows peripheral device approach, and appraises piles of secondary mother amassed amidst network and academic databases namely, dissertation, websites, edition, periodicals, news from plausible origin, etc. for implementation of AI in higher education. Data collection strategies wielded in the paper are cognizance and study. Data is interpreted and then deduced. Revision, codification and categorization of material wast accomplished against requirements for the study. The paper addressing secondary feed adheres to overall ecstasy of AI in higher education not on any specific country or region. The oeuvre of this paper is limited to corroborate, incipiently, efficacy of AI. Then, an inquisition on the intrinsic chare travailing pros and cons along with protective umbrella for AI in higher education has been detailed apropos peripheral information and researcher’s own contemplation. The researcher has beguiled AI in higher education as AI is crescendoing and metamorphosing the lineament of higher didactics. Modus operandi of reasoning is productive for excogitating AI in tertiary education.

LITERATURE REVIEW:

Educational personalization means a mode of intercommunication of participators when learners amass culturally relevant, distinct or individualities besides attribute to cogently execute societal obligation (Osadcha et al., 2021)². With AI, students can learn anytime appositely that tailors their cognitive approach (Srivastava et al., 2023)¹. Experts in domain of educational technology prognosticate that in future modest adaptation of AI will switch textbooks and grasp cognitive development off campus (Lopez-Belmonte, 2020)³. AI implements individualized learning, permits huge data and fabricates conclusions to progress intellectual orientation concocting specific requirements of students (Kocharian and Viktorova,2021)⁴. Research signalizes disassociation between AI techniques and their theoretical foundations weighing consequences on AI practices in literacy (Ouyang and Jiao, 2021)⁵. AI in education contributes novel embryonic to upgrade insight gained (Chiu et al., 2023)⁶. Virtual assistants and chatbots transmit briskly, personalized, affordable and foolproof methods (Dhawan and Batra, 2021)⁷. AI enhances students’ learning outcomes, lessens dropping out and fabricates individualized learning environ in higher education (Pedro, 2020)⁸.AI affords interactive admonishment to cultivate bird’s eye digitalization approach for higher education and researchers to conclude their inquest utilizing technology ecosystem to concur globally with other researchers (Papaspyridis, 2020)⁹. AI prognosticates students’ susceptibility of dropout and its’ rationale, and feeds elucidations (Tsai et al.,2020)¹⁰. For thriving incorporation of AI into teaching-learning, instructors must cultivate AI literacy, decipher closeness with students-teachers, and surmount varied obstacles (Chan and Tsi,2023)¹¹. With escalating relevance for harnessing ChatGPT in higher education, students have initiated its’ exercise to prepare works, devise codes, reckon mathematical explanations, etc. (Halaweh, 2023)¹². Baker (2021)¹³ demonstrated idea of using AI for homework-based feedback. Homework tools help instructors locate every student’s predicament. Instructors can remold directions and materials for students’ benefit. Teachers confirm students’ difficulties and arrange personalized activity. Pabba and Kumar (2021)¹⁴ in a study inferred that AI mechanism productively pinpoints learners’ position and modifies lesson for better outcome. Personalized learning with AI offers pliability as to accelerating with arranging. Learners perform independently and, thus, minimize stress and anxiety (AlAli et al., 2023)¹⁵. Personalized learning locates students' instinctive attributes, thirsts and attainments (Samad, 2022)¹⁶. AI develops systems necessitating modern brain to accomplish responsibility claiming potency of determination and perception (Aldosari, 2020)¹⁷. Auto-grader program evaluates students’ works without human intrusion besides grading multiple-choice tests. Outcomes vary from binary to hypothetical particulars (August and Tsaima, 2021)¹⁸. Individualized evaluative test is constructive format of learning feedback for students, particularly apropos MOOCs and other online learning environments (Gardner et al.,2021)¹⁹.

AI-Concept:

AI elucidates replication of human ingenuity in machines intended to ruminate and perform like humans. It apropos of its strength to perpetrate functions like studying, brainstorming and governing embraces diverse technologies namely, machine learning, knowledge engineering, robotics, etc. AI system featured by machines exacting human intelligence now behaves with humanism constantly in every sector from medicaments to banking in committing rationality, cognition and decisiveness. It is a computational approach exhilarated by humankind to realize, study and conduct. AI embodies usage of algorithms that acknowledging data layout and learning therefrom, extrapolate when given fresh thoughts (Ramlakhan et al., 2022)²⁰. Breakthrough of AI expounds big moral and ethnic dilemmas, including outplacement, privateness and demand for legal base to clinch its responsibility.

RESULTS AND DISCUSSIONS:

Opportunities:

Improved Engagement: AI enhances students’ engagement by contributing substance to their enthusiasm. This fosters close liaison yielding overflowing impetus and better learning outcomes. Multimedia and gamification techniques enliven learning.

Policy-making Advisor: Development of policy-making advisors in higher education is prudent. AI technology helps policymakers sense direction and complication within academic bodies from macro-micro perspective that advocates in fabricating and reckoning implicit educational policies.

Intelligent Tool: Intelligent learning tool help learners collect and analyze data constructively, entitle them to hearken critical points beyond tangential exercises. Even few tools analyzing data astutely assist learners to ruminate and explore worthwhile connotations.

Skilled Tutee and Tutor: AI models help learn knowledge from interactions with humans. Cognitive skills facilitate evolution of skilled tutees and tutors. AI in Education (AIEd) installs novel paradigm for pedagogical scenario and advanced technology learning environments.

Educational Management Information System (EMIS): EMIS upgrades monitoring to safeguard accountability, transparency and creativity amidst students-teachers. This stimulates data processing and its integrity for furthering deployment of higher education. Authority can also actuate to integrate technology and humankinds (Sauphayana, 2021)²¹.

In-depth Analysis: AI performs in-depth analysis of huge data operating neural networks with clouded intensity. Deep learning models require data intensive depending on which students learn. More the data, more accurate the model.

Feedback: AI-based programs are springs of feedback to both learners and teachers. AI systems propitiously reconnoiter learners’ performance and duly communicate teachers about ongoing challenges. Such systems build environment for cognitive advancement and suitable germane variations. Programs help students favor the specialty where they flourish.

Heads of Institutions: AI assists heads to contrive stratagem for cooperation that maximizes blended dexterity of staff. Mentoring and brainstorming are, thus, developed midst one-way door to quantitative and qualitative data. Heads have access to assessment of edata that empowers persistence and adoption of black-ink items for sustainable development.

Involvement and Communication: AI promotes involvement of parents and local governments for resolution, sustainable development, administration of higher education, etc.

Teachers: Prolific pertinence of AI technologies feeds teachers’ opportunity to access teaching close-at-hand. Teachers can fixate cooperation and professional development. Teaching-learning becomes breathtaking in virtual environment with clockwork.

Source of Expertise: AI metamorphoses teachers persistently and exuberantly. It heightens learners’ learning and even replaces real learning. AI system is fountain of expertise whither students present their questions, or even approach teachers for crucial questions.

Dereliction Proclivity: Cognitive ideas prevail snag within tutelage and also an emerging challenge in AI means. Offing of AI in study should utilize explications that can evaluate work accepting stable ethos to automate culmination for dispassionateness.

Delineation: Students’ needs and curriculum priorities are dynamic. Content provisioned through educators is pertinent. AI Analytics in education distinguishes macro undertow and helps teachers develop persuasive study room that triggers digitization.

Recognition: Data analysis favors perceiving that adaptive AI recognizes essence for learners. Invulnerable security can handle formation enigma.

Customized Instruction: AI interprets students’ data and produces customized learning trajectory to cater to each student's exigencies and dexterity. This elevates academic proficiency.

Upgraded Retention: AI improves retention rates by tailor-making students’ pros and cons, and scholastic fascinations (Al-Bahrani and Cree,2021)²². Contemplated feedback and bespoke academic itinerary furnish students’ retention information better and build their knowledge over time. Tailored object lesson shows upturn in achievement rates whereupon students collect text as per their aptitude(Jarrah et al., 2022)²³.

Curriculum Customization: AI expedites in executing lesson prelude, also helps towards progressing course intents, and creation of concordant fiefdom and precipitate with module lessons. Integrating AI into curriculum, students acclimatize its’ principles and applications in several facets.

Research Environment: Enactment of AI brings avant-garde learnscape letting students’ research edifice or ambience in welcoming environment. Research tools support students and faculty gather apposite potentials and stimulate ideas. Blistering technology yields encyclopedic in teaching with endless opportunities.

Easy Interaction: AI Dubbed ‘teacher-bot’ receives students’ questions and response, thereby promotes interaction. University staffs devote monstrously to collaborate students and instigate discussion. Interactive content brings effective learning outcomes. AI-powered virtual tutors improve knowledge gaps and provide hands-on experience.

Personalized Approach: AI-enhanced assessment tools diagnose students’ pros and cons, and offer suggestions for improvement. AI learning style is towards the needs of students, not students fit towards the needs of the system. This facilitates borderline students’ ventilation while elite belts can be challenged at their stratum. Personalized enriching lesson betters academic performance and retention rates.

Automated Grading: AI automated grading system contributes students’ split-second feedback built on predestined barometer and buys educators’ time and potency (Ji et al.,2022)^[24]. Automated Essay Scoring Systems provide in-depth feedback and in particular, students can revise paper before submission for gradation.

Language Learning: AI language learning tools offer personalized, efficient and accessible ways to acquire new languages. Emotion recognition technology stays students’ ardent about deepening learning tenor. Duolingo English Test helps teachers originate live conversation lessons of any language.

Effective Presentation: AI assists in effective presentation. Machine learning tools concede teachers to effect special unique content and arrange titles and subtitles. AI-powered software searches various websites and initiates detailed paper shortly.

Visualization: Skilled 3D Learning tools let students adopt interactive visualization-based learning knowledge. Wolfram Alpha generating visualizations of mathematical functions, graphs and equations discerns intricate mathematical themes. Microsoft Mesh accredits to communicate, develops projects and probes new hypothesis in digital landscape.

Adaptive Learning: AI-powered mobile apps interpret learning success, track students’ progress and identifies distressing areas. Educators adjust their approach and students perfect their awareness.

Better Data Analysis: Mobile apps can study students’ test scores, academic standing and attendance records to detect learning turns and motifs. By interpreting big data, AI elucidates students’ performance, admits teachers to better comprehend students and moulds their guidance. Teachers originate collaborative curriculum to meet students’ needs constructively. This improves learning outcomes.

Challenges:

While AI has pizzazz in holistic education, it also presents challenges. The following are certain blowbacks:

Over-reliance: Excess reliance on AI technology causes over-dependence that lessens consistency and pliancy against unexpected changes. Fundamental human interaction in education dies down.

Reliability and Errors: AI clinches stemming from humans’ thoughts. Quality of data affects accuracy of personalized lessons. Need arises for reliable data to inform algorithms. Imprecise algorithms make mistakes in assessment or thoughts. Therefore, it is crucial to vouch reliable and up-to-date data.

Digital Division: AI deepens digital division betwixt students’ accessibility and inaccessibility to technology. Absence of ensuring equitable access makes impoverished students dawdle.

Teacher Role Replacement: Plethoric manoeuver of AI in education weakens teachers’ role. Human interactions about social perception and intricate contextual appraisal are imperative. Balance between the roles of AI and humans is nonelective.

Privacy and Security: AI addresses questions about data privacy and security, and poses challenge if it ingresses hostile hands. Many are found unwilling to share personal data due to privacy concern. Institutions must substantiate students' privacy and prevent breaches. Students' personal details and study inputs must be fended from mishandling (Yeruva, 2023)²⁵.

Trust: Learners prefer feedback prepared by human to AI technique. Building trust senses students more comfortable with technology.

Cost: Cost is a challenge for educational institutions meeting budget limitations. Institutions must excogitate boon and bane of executing AI approach in their classrooms.

Potential Devices: Intelligent retrieval maybe distorted, principally unless it is prepared on preconceived idea emanating injustice towards certain students and inadvertently perpetuate existing inequalities. Institutions must ensure impartial autodidact for all students, and discourage existent disparity.

Ethical Considerations: Decisions built on AI approach sporadically uplift ethical questions. AI technology must be sunshiny promising anti-discrimination with everybody. Every student including disabled must get assurance to encroach technology. Diaphanous ethical code ensures cool idea and deliberates human aspects.

Inflated Bigotry: Inflated bigotry is another issue between wealthy and poor institutions. Wealthier institutions broaden AI-based learning faster than poor succeeding students of wealthier institutions more technologically proficient than poor.

Lacking Self-Trimming: AI often operates better than human brain but it lacks self-trimming crucial to strengthen veracity.

Inequality: AI-based models adapt racial prejudice data to train tailor-made learning and placement algorithms, thus emplacing minorities at downsides. Moreover, financially challenged institutions cannot afford convoluted AI security systems or AI teacher due to high price. Price causes inaccessibility for this newest technology.

EMIS: Majority of EMIS in higher education is ill-fated. Financial strains make it challenging to pioneer. Embracing EMIS is pricey for development and accomplishment.

Students’ Assessment: Modern AI modes designed to identify students’ specific task fail to assess identical students. Modes configured to detect plagiarism pretermit e-learning. These systems are far from human multitasking. Modes based on specific expertise persist without human multitasking.

Professional Development: Integrating individualized instruction with formal education is troublesome. Educators exact for appropriate employment of AI mechanism inside classes (Yeruva, 2023)²⁵. This develops professional skills.

Student-Centered Approach: Designing chatbots with student-centered approach is a challenge to cinch students' exigencies, passions and cognitive modes. Supporting accessibility to accepting technology for all is badly needed. Another challenge is to ensure reliable chatbots and present precise details conscientiously.

Preventions:

Balance between Benefits and Challenges: Knowledge engineering expects well-balanced proposition. Through balancing benefits and challenges of using AI, a sustainable approach must be implemented.

Strategy Betterment: Governments with academic bodies should better protean courses alongside blueprints about data secrecy and safety, ethics, etc. weighing socio-economic challenges. By admitting internet devices, governments must step forward to link arms among social classes to attain rectitude and sociability.

2

Capacity Building: Capacity building requires passable training for overcoming demand for skilled staff on using EMIS. They should behold deployment of AI efficaciously in pedagogical and official approaches.

Assessment Mechanism: Constant assessment of AI tool is pressing to safeguard cogency. Assessment is feasible through assaying learners’ performance, staffs’ happiness, suasiveness of tool’s execution.

2

Privacy and Security Compliancy: AI systems must greet meticulous materials confidentiality and protection tenets. Frequent regulation drives must be enforced. Institutions should maintain ethics during its’ execution in higher study.

2

Human-robot interaction: Restoring fragile equipoise of humans with AI is must-have. Abyssal social relationship and fostering emotional attachments loom large in impressive academic process.

Decision-making Technique: Decision-making procedure for culling specific AI technology must be cognizant, methodical and culpable.

Cybersecurity: Database establishes AI’s puissance. Big data continues impassive; recent cybersecurity research should obligate basic step forward to this issue.

Bulk Data: Assuring dynamic concretization needs maximum data obtainment about student, including social networking activities, search history, geolocalization and others.

Sufficient Research: Provided poor research on risk of viewing AI in teaching-learning process, discreteness of accountabilities for access, storage and operation of clandestine data is suggested.

Campus Planning: Campus planning is effective to overcome challenge. AI appliance like digital teaching-aids and smart campus suffices to overcome challenges (Akinwalere and Ivanov, 2022)²⁶.

Versed Public Policy: Efficient public policy let everybody fraternize for enhancing AI application in higher education. States should sufficiently support educational institutions for reshaping teaching-learning, and build academic centers of excellence for research and cool confidence to patronize embracing AI.

Language Barriers: Sponsoring to higher education for AI chatbots to savvy multiple languages can prevent language barriers for seamless transition. AI chatbots to institutions websites can admit kith ineloquent in English to be informed anytime and cherish their students. Students can interrogate in chatbots howbeit teacher remains absent.

Technological Infrastructure: States should concoct novel gimmick for refurbishing AI educational facilities to institutions lacking basic tech framework like latest power devices, computer, net service, technical prowess, data expense, etc.

Inventive Administration Skills: Institutions should amass creative administration competences to steer manpower and AI. AI tools help learners accomplish new expertise and instinct.

Data Collection and Systematization: Inclusive data analytics system helps higher education in data collection and systematization. Governments must initiate academic administration structure offering exhaustive quality data methodology.

AI in higher education: predictions and possibilities:

AI prognosticates to progress revamping teaching-learning and curriculum design. Predictive analytics promote early intervention through identifying vulnerable students in specific areas. Educators need training for exercising AI valuably and surmising its’ contingency. Teachers should devote themselves as academics, effect to approach with students, and collaborate with colleagues to discuss what works and what does not. AI in higher education has promising future with unique opportunity for evolution. Intelligent retrieval has occasion to modify the recipe of teaching-learning pragmatic, mold resilient for students. In future, more sophisticated AI technology can construe to human intuition, enunciate more fastidious proposition, and truly spawn individualized lesson plans for each student. Journey to integrating AI is problematic one loaded by pluses and minuses. But with positive thinking, academic can voyage this swimmingly to meliorate academic pursuits while keeping heartfelt touch in education.

CONCLUSION:

AI in higher study remains potentiality to regenerate teaching-learning technique and institutional operation. Institutions can individualize learning opportunity, relax endeavor and equip students with proficiency quested in market. However, embracing robotization in tertiary learning continues still incipient. Multiform dismays need to address like insubstantial infrastructure, digital addiction, ethical conundrum, nebulous stratagem, etc. Contrary to expectation, fortuitous perks of AI cannot turn. Institutions should espy its plausibility in better education and pay close attention to implementation with AI and fence students' clandestinity and chaperone anti-discrimination towards students. Tailing AI in higher education stipulates well-thought-out overture. By optimizing dos and don’ts of AI, authority can tap its’ opportunities to hit bull’s eye of sustainable education. Its’ diaphanous mileage reflects interests to all embroiling mineduc, and will perdure as puissant stimulus for higher education reform.

IMPLICATIONS OF THE STUDY:

The study evinces flowering fervor of AI in higher education. Disciple-faculty nurtures chef-d’oeuvre foreboding, exertion heartening and cultural clout. However, there are still few eye-catching areas, particularly fostering requirements and deliberate menace. This study represents as optic for student-teacher in higher education to eye faculty’s response over AI in teaching-learning and operate this as a pilot to initiate its’ integration in institutions. Also, with ever-growing demand for higher education, AI can woo broad dissemination for higher study. Thus, decisor can pursue to plan AI-led humbling experience for everybody.

FUTURE RESEARCH:

Future research encompasses studying students’ perception since main objective behind espousing perception betters insight to phenomena. More views can be ruminated from society to extrapolate espials. AI tool wrought in indoctrination maybe examined separately. Besides, it’s implementation to garner einclusion has possibility for further persuasion apropos diverse types of disablement incongruously. Cutting-edge AI technology emerges daily. So this subject field is just trailblazing and clock is still ticking. Peripheral inquisition yet implores to tergiversate burning matters. Further study concerning pedagogical ideologies embedded in genesis and state contrast may endow AI in education boomier. Added research focus covers application of AI in administration dream, not exclusively for pedagogy.

RESEARCH COMMENT:

A waving towards privacy-preserving models and unobtrusive AI technique can tolerate its’ mainstream application overseas. Sky-high AI literacy in higher education can soothe true-blue practice of AI to both academics. Further, broad-spectrum germanenesses of avant-garde AI technology avow to have big impact on higher education and also wherewithal in academic climate. AI can bless profoundly through eco-friendly advent. With boosting godsend and micromanaging springing concerns as to AI, higher learning should welcome green light in triumphing educational dream and providing persuasive rewarding lesson for students.

CONFLICT OF INTEREST:

No.

REFERENCES:

1. Shrivastava, A., Suji Prasad, S. J., Yeruva, A. R., Mani, P., Nagpal, P., Chaturvedi, A. IOT based RFID attendance monitoring system of students using Arduino ESP8266 and Adafruit. io on defined area. Cybernetics and Systems. 2023; 1-12: https://doi.org/10.1080/01969722.2023.2166243.

2. Osadcha, K., Osadchyi, V, Spirin, О., and Kruhlyk, V. Realizatsiia indyvidualizatsii ta personalizatsii navchannia zasobamy Moodle [Implementation of individualization and personalization of learning by means of Moodle]. Youth and The Market. 2021; 1:187. https://doi.org/10.24919/2308-4634.2021.228274

3. Lopez-Belmonte, J., Moreno-Guerrero, A., Lopez-Nunez, J., Hinojo-Licena, F. Augmented reality in education. A scientific mapping in Web of Science. Interactive Learning Environments. 2020; 1-15. https://doi.org/10.1080/10494820.2020.1859546

4. Kocharian, А., Viktorova, L., Kostiantyn, M., and Olena, Korotun. Artificial intelligence and chatbots application in foreign language learning. Innovative Pedagogy. 2021; 32(2): 166-173. https://doi.org/10.32843/2663-6085/2021/32-2.33

5. Ouyang, F., Jiao, P. Artificial intelligence in education: The three paradigms. Computers and education: Artificial Intelligence. 2021; 2:100020. https://doi.org/10.1016/j.caeai.2021.100020

6. Chiu, T. K. F., Xia, Q., Zhou, X., Chai, C. S., Cheng, M. Systematic literature review on opportunities, challenges, and future research recommendations of artificial intelligence in education. Computers and Education: Artificial Intelligence. 2023; 4:100118. https://doi.org/10.1016/j.caeai.2022.100118

7. Dhawan, S., Batra, G. Artificial intelligence in higher education: Promises, perils, and perspective. Expanding Knowledge Horizon. OJAS. 2021; 11:11-22. https://www.researchgate.net/publication/ 348910302

8. Pedró, F. Applications of artificial intelligence to higher education: Possibilities, evidence, and challenges. IUL Research. 2020; 1(1):61-76. https://doi.org/10.57568/iulres.v1i1.43.

9. Papaspyridis, A. AI in higher education: Opportunities and considerations. 2020. https://news.microsoft.com/apac/2020/03/26/ ai-in-higher-education-opportunities-and-considerations/

10. Tsai, S., Chen, C., Shiao, Y., Ciou, J., Wu, T. Precision education with statistical learning and deep learning: A case study in Taiwan. International Journal of Educational Technology in Higher Education. 2020; 17: 12. https://doi.org/10.1186/s41239-020-00186-2

11. Chan, C., Tsi, L. The AI revolution in education: Will AI replace or assist teachers in higher education? Computers and Society. 2023. https://doi.org/https://doi.org/10.48550/arXiv.2305.01185

12. Halaweh, M. Chatgpt in education: Strategies for responsible implementation. Contemporary Educational Technology. 2023; 15(2): 1–11. https://doi.org/10.30935/cedtech/13036

13. Baker, R. Artificial intelligence in education: Bringing it all together. In OECD Digital Education Outlook 2021: Pushing the Frontiers with artificial intelligence, Blockchain and Robots (pp. 43-56). OECD Publishing. https://doi.org/10.1787/589b283f-en

14. Pabba, C., Kumar, P. An intelligent system for monitoring students' engagement in large classroom teaching through facial expression recognition. Expert Systems. 2021; 39(1): 1-28. https://doi.org/10.1111/exsy.12839

15. Al Ali, R., Wardat, Y., Al-Qahtani, M. SWOM strategy and influence of its using on developing mathematical thinking skills and on metacognitive thinking among gifted tenth-grade students. Eurasia Journal of Mathematics, Science and Technology Education. 2023; 19(3): em2238. https://doi.org/10.29333/ejmste/ 12994

16. Samad, Abdul. Antibiotics resistance in poultry and its solution. Devotion Journal of Community Service. 2022; 3(10): 999–1020. https://doi.org/10.36418/dev.v3i10.206.

17. Aldosari, S. A. The future of higher education in the light of artificial intelligence Transformations. International Journal of Higher Education. 2020: 9(3): 145-151. https://doi.org/10.5430/ ijhe.v9n3p145

18. August, S. E., Tsaima, A. Artificial intelligence and machine learning: An instructor’s exoskeleton in the future of education. Innovative Learning Environments in STEM Higher Education. 2021; 79–105. https://doi.org/10.1007/978-3-030-58948-6_5

19. Gardner, J., O'Leary, M., Yuan, L. Review for artificial intelligence in educational assessment: ‘breakthrough? or buncombe and ballyhoo?. Journal of Computer Assisted Learning. 2021; 37(5): 1207–1216. https://doi.org/10.1111/jcal.12577

20. Ramlakhan, S., Saatchi, R., Sabir, L., Singh, Y., Hughes, R., Shobayo, O., Ventour, D. Understanding and interpreting artificial intelligence, machine learning and deep learning in emergency medicine. Emergency Medicine Journal. 2022; 39(5): 380–385. https://doi.org/101136/emermed-2021-212068

21. Sauphayana, S. Innovation in higher education management and leadership. Journal of Educational and Social Research. 2021; 11(6): https://doi.org/10.36941/jesr-2021-0137

22. Al-Bahrani, M., Cree, A. In situ detection of oil leakage by new self-sensing nanocomposite sensor containing MWCNTs. Applied Nanoscience. 2021; 11(9): 2433-2445

23. Jarrah, A. M., Almassri, H., Johnson, J. D., Wardat, Y. Assessing the impact of digital games-based learning on students’ performance in learning fractions using (ABACUS) software application. Eurasia Journal of Mathematics, Science and Technology Education. 2022; 18(10): em2159. https://doi.org/ 10.29333/ejmste/12421

24. Ji, Li, Jun, Chen, Zhi, Yuan, Lei, Xu, Yuying, Zhang, Mohammed, Al-Bahrani. Multi-objective risk-constrained optimal performance of hydrogen-based multi energy systems for future sustainable societies. Sustainable Cities and Society. 2022; 87(1): 104176. https: doi.org/10.1016/j.scs.2022.104176

25. Yeruva, A. R. (2023). Monitoring data center site infrastructure using AIOPS architecture. Eduvest-Journal of Universal Studies. 2023; 3(1):265-277. https://doi.org/10.36418/eduvest.v3i1.732.

26. Akinwalere, S. N., and Ivanov, V. Artificial intelligence in higher education: Challenges and opportunities. Border Crossing. 2022; 12(1): 1–15. https://doi.org/10.33182/bc.v12il.2015

Received on 27.07.2024 Modified on 24.08.2024

Asian Journal of Management. 2024;15(3):238-244.

DOI: 10.52711/2321-5763.2024.00037