IJHEM 2054-9849 2054-9857 https://doi.org/10.24052/IJHEM/ 15 Volume 08 Issue 02 2022-02-01

87 https://ijhem.com/details&cid=87 2022-02-28 Chat-bot as an (AI) technology has taken great attention in recent years and especially in the education sector. before applying such new technology. it is vital to Understand the determinates that affect the behaviour intention for a student to accept or reject this technology in higher education, to understand this behaviour intension, the current research applied the unified theory of acceptance and use of technology (UTAUT) with excluding for two moderators from the original model which are experience and Voluntariness of use. Additionally, this research excluded facilitating conditions and behaviour use as it aimed to investigate only the intension behaviour of the students. This study also aimed to examine the role of demographic factors (gender and age) effect on the model research independent variables and the behaviour intension variable. Therefore, the researcher put the objectives of the study that are represented in, developing a framework for the acceptance of chat-bot technology on the behaviour intension of students in higher education in Egypt. To achieve these objectives, the researcher collects data about the required variables by making a questionnaire. This questionnaire targeted students at the Arab academy for science and technology and maritime transport (AASTMT). AASTMT was selected because it represents one of the oldest private universities in Egypt that apply artificial intelligence technology in its educational system. The final sample consisted of 385 responses. data were analyzed through data testing, descriptive analysis, correlations, regression, and structural equation modelling (SEM). Results indicated a significant impact of performance expectancy, effort expectancy and social influence on students' behaviour intention to accept the chat-bot technology in their higher education in Egypt. Moreover, the results have shown that there is no moderating role of demographic factors (gender and age) proved in the relation between performance expectancy, effort expectancy, social influence, and behaviour intention Al-Mamary YH, Shamsuddin A, Hamid A, Aziati N. Investigating the key factors influencing management information systems adoption among telecommunication companies in Yemen: the conceptual framework development. International Journal of Energy, Information and Communications. 2015;6(1):59-68. Alharbi, S. and Drew, S. (2014). Using the Technology Acceptance Model in Understanding Academics’ Behavioural Intention to Use Learning Management Systems. International Journal of Advanced Computer Science and Applications, 5(1). Alshboul, Khaled & Bardai, Barjoyai & Alzubi, Mohammad. (2018). the moderating effects of demographic factors in the usage of e-government services among jordanian citizens. International Journal of Humanities and Social Science. 6. 184-199. Bentler, P. M., and Chou, C.-P. (1987). Practical issues in structural modelling. Social. Methods Res. 16, 78–117. DOI: 10.1177/0049124187016001004. Boomsma, A. (1985). Nonconvergence, improper solutions, and starting values in LISREL maximum likelihood estimation. Psychometrika50, 229–242. DOI: 10.1007/BF02294248. Brinton, C., Rill, R., Sangtae, H., Mung, C., Smith, R. & Ju, W. (2015). Individualization for Education at Scale: MIIC Design and Preliminary Evaluation. IEEE Transactions on Learning Technologies, 8(1), pp.136–148. Burns, M.E., Houser, M.L. and Farris, K.L. (2017). Theory of planned behaviour in the classroom: An examination of the instructor confirmation-interaction model. Higher Education, 75(6), pp.1091–1108 Carter, L., and F. Belanger, “The utilization of e-government services: citizen trust, innovation and acceptance factors” Information Systems Journal, (15:1), 2005 Chatterjee, S. and Bhattacharjee, K.K. (2020). Adoption of artificial intelligence in higher education: a quantitative analysis using structural equation modelling. Education and Information Technologies, 25(5), pp.3443–3463. Chrisinger, D. (2019). The solution lies in education: Artificial intelligence & the skills gap. On the Horizon, 27(1), 1–4. https://doi.org/10.1108/OTH-03-2019-096. Cremer, D., & Bettignies, H. C. (2013). Pragmatic business ethics. The Leadership Maestro, 24(2), 64–67. https://doi.org/10.1111/j.1467-8616.2013.00938.x.              Creswell, J.W., 2009. Mapping the field of mixed methods research. Cronan, T. P., Mullins, J. K., & Douglas, D. E. (2018). Further understanding factors that explain freshman business student's academic integrity intention and behaviour: Plagiarism and sharing homework. Journal of Business Ethics, 147(1), 197–220. Dutta, D. 2017. Developing an Intelligent Chatbot Tool to assist high school students in learning general knowledge subjects. Georgia Institute of Technology. Atlanta Foon, Y. S., & Fah, B. C. Y. (2011). Internet banking adoption in Kuala Lumpur: An application of UTAUT model. International Journal of Business and Management, 6(4), 161–167 Gerbing, D. W., and Anderson, J. C. (1985). The effects of sampling error and model characteristics on parameter estimation for maximum likelihood confirmatory factor analysis. Multivariate Behav. Res. 20, 255–271. DOI: 10.1207/s15327906mbr2003_2 Hair, Joseph & Black, William & Babin, Barry & Anderson, Rolph. (2010). Multivariate Data Analysis: A Global Perspective. Holmes, Mary. (2008). Higher education reform in Egypt: Preparing graduates for Egypt's changing political economy. Education, Business and Society: Contemporary Middle Eastern Issues. 1. 175-185. 10.1108/17537980810909797. Huang, J.-X., Lee, K.-S., Kwon, O.-W., & Kim, Y.-K. 2017. A chatbot for a dialogue-based second language learning system. CALL in a climate of change: adapting to turbulent global conditions: 151 Kerly, A., Hall, P., & Bull, S. 2007. Bringing chatbots into education: Towards natural language negotiation of open learner models. Knowledge-Based Systems, 20(2): 177–185.                           Khasawneh, M., & Ibrahim, H. (2012). A model for Adoption of ICT in Jordanian Higher Education Institutions: An Empirical Study. Journal of e-Learning & Higher Education, 2012, c1-10. Khechine, H., Pascot, D., Lakhal, S., & Bytha, A. (2014). UTAUT model for blended learning: The role of gender and age in the intention to use webinars. Interdisciplinary Journal of ELearning and Learning Objects, 10, 33-52. Retrieved from http://www. ijello.org/Volume10/IJELLOv10p033-052Khechine0876.pd Kozma, R. (2004). Technology, economic development, and educational reform: Global changes and an Egyptian response. Arlington, VA: Pal-Tech. Krejcie, R. V., & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30, 607-610. Lidén, A. and Nilros, K. (2020). Perceived benefits and limitations of chatbots in higher education. [online] Available at: http://www.divaportal.org/smash/get/diva2:1442044/FULLTEXT01.pdf. Magsamen-Conrad, K., Upadhyaya, S., Joa, C. Y., & Dowd, J. (2015). Bridging the divide: Using UTAUT to predict multigenerational tablet adoption practices. Computers in Human Behavior, vol. 50, pp.186-196. Maldonado, Ursula & Khan, Gohar & Moon, Junghoon & Rho, Jae. (2011). E-learning motivation and educational portal acceptance in developing countries. Online Information Review. 35. 66-85. 10.1108/14684521111113597. Menon, R., Tiwari, A., Chhabra, A., & Singh, D. (2014). Study on the higher education in India and the need for a paradigm shift. Procedia Economics and Finance, II, 1, 886–871. https://doi.org/10.1016/S22125671(14)00250-0. Sana'a Y. A critical review of models and theories in field of individual acceptance of technology. International Journal of Hybrid Information Technology. 2016;9(6):143-58 Sekaran, U. and Bougie, R., 2016. Research methods for business: A skill-building approach. John Wiley & Sons. Stefan, A. D. P., & Sharon, K. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 1, 313. https://doi. org/10.1186/s41039-017-0062-8. Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D.: User acceptance of information technology: Toward a unified view. MIS Quarterly 27(3), 425–478 (2003). Wang, Y., Forbes, R., Cavigioli, C., Wang, H., Gamelas, A., Wade, A., Strassner, J., Cai, S. and Liu, S., 2018. Network management and orchestration using artificial intelligence: Overview of ETSI ENI. IEEE Communications Standards Magazine, 2(4), pp.58-65. Winkler, R. & Söllner, M. (2018). Unleashing the Potential of Chatbots in Education: A State-Of-The-Art Analysis. Academy of Management Annual Meeting (AOM). Chicago, USA. Yasmeen, S., Alam, M.T., Mushtaq, M. and Alam Bukhari, M. (2015). Comparative Study of the Availability and Use of Information Technology in the Subject of Education in Public and Private Universities of Islamabad and Rawalpindi. SAGE Open, 5(4), p.215824401560822. https://ijhem.com/cdn/article_file/2022-02-28-20-55-56-PM.pdf Mohamed A. Ragheb Passent Tantawi Nevien Farouk Ahmed Hatata Performance expectancy effort expectancy demographics chat-bot behaviour intention UTAUT 15753 134 52

88 https://ijhem.com/details&cid=88 2022-02-28 Major conflicts and wars are the fundament and important drivers for strong economies and their expansions. Military institutions are the ones who drive major technological transformation, evolution and innovation trends; like the USA’s Défense Advanced Research Projects Agency (DARPA), which developed the Internet… Military technology transformation and innovation projects depend on financing capacities, geopolitics, economical strategies and demography. Countries, Armies, and institutions (or simply Entities) are increasingly using avant-garde technologies to gain substantial defence, geopolitical and economic advantages. Entities are today, facing new challenges and eventual risks, when implementing their vital organizational defence concept and distributed Information and Communication System (ICS). It is important to find the right balance between, Optimal Innovative Military Technology (OIMT), biotech’s evolution, military strategy, geopolitical context, combative capabilities, and the evolution of demography, which is probably the most important factor. The stability of an Entity depends on a well-defined OIMT Strategy to support the Entity’s evolution. This article proposes the fundaments of Artificial Intelligence (AI) to support Virtual Reality (VR) for OIMT (VR4OIMT) integration. The author’s Applied Holistic Mathematical Model (AHMM) for VR (AHMM4VR) is the result of research on AI-based VR, business, financial and organizational transformations using a mathematical model’s concept (Trad & Kalpić, 2020a). The AI based VR4OIMT manages and evaluates VR activities in projects, which are complex. Weightings of factors and areas are used as variables in the VR4OIMT. VR’s main problem is peoples’ addiction and subject areas’ complexities, due to the excessive use if VR based videogames which have exponentially expended and making decisions based on simple simulations (Acer, 2018) Acer (2018).  Education Trends-How Education can benefit from vActivity. Acer. https://acerforeducation.acer.com/education-trends/how-education-can-benefit-from-vActivity/, accessed on 18-FEB-2021. Alton, L. (2019). An Introductory Guide to vActivity Technology. Connected. https://community.connection.com/an-introductory-guide-to-vActivity-technology/ Bailey, K. (2012). What would my avatar do? Video games and risky decision making. Graduate Theses and Dissertations Iowa State University Capstones, Theses and Dissertations. Iowa State University. USA. Cheyney, S. (2021). Strategy and Tactics, Military. Scholastic Inc. USA. Daellenbach, H., McNickle, D. & Dye, Sh, (2012). Management Science - Decision making through systems thinking. 2nd edition. Palgrave Macmillian. USA. Della Croce, F., & T'kindt, V. (2002). A Recovering Beam Search algorithm for the one-machine dynamic total completion time scheduling problem, Journal of the Operational Research Society, 53:11, 1275-1280. Taylor & Francis. Easterbrook, S., Singer, J., Storey, M., & Damian, D. (2008). Guide to Advanced Empirical Software Engineering-Selecting Empirical Methods for Software Engineering Research. F. Shull et al. (eds.). Springer. Einstein, A., & Shaw, G; (2012). Cosmic religion, With other opinions and aphorisms. Dover Publications. International Monetary Fund (2009). Switzerland: Financial Sector Assessment Program - Detailed Assessment of Observance of Financial Sector Standards and Codes. International Monetary Fund, 5. kol 2009. - Page: 170. Jonkers, H., Band, I., & Quartel, D. (2012a). ArchiSurance Case Study. The Open Group. Kania, E., & Vorndick, W. (2019). Weaponizing Biotech: How China's Military Is Preparing for a 'New Domain of Warfare. Government Media Executive Group LLC. Krasner, E. (2020). How to choose between rule-based AI and machine learning. TechTalks. Le Baher, H. (2019). Data Science as support of vActivity performance and strategies (I)-First step : A general case study about the League of Legends World Championship. Towardsdatascience. Mees, W. (2017). Security by Design in an Enterprise Architecture Framework. Royal Military Academy, Department CISS. Renaissancelaan 30, 1000 Brussel. NATO. Belgium. Leitch, R. & Day, Ch. (2000). Action research and reflective practice: towards a holistic view. Taylor & Francis. Queen's University of Belfast, United Kingdom. Retrieved February 10, 2018, from https://www.tandfonline.com/doi/ref/10.1080/09650790000200108?scroll=top  Merriam-Webster (2020a). intelligence. Intelligence | Definition of Intelligence by Merriam-Webster. Myers, B., Pane, J. & Ko, A. (2004). Natural programming languages and environments. ACM New York, NY, USA. Open4Tech (2019). Trees vs. Graphs. Open4Tech. https://open4tech.com/trees-vs-graphs/ Potier, A. (2020). NATURAL LANGUAGE PROCESSING: HOW AI UNDERSTANDS OUR LANGUAGES. Konverso. Rudolf, K., Bickmann, P., Froböse, I., Tholl, Ch., Wechsler, K., & Grieben, Ch. (2020). Demographics and Health Behavior of Video Game and vActivity Players in Germany: The vActivity Study 2019. National Center for Biotechnology Information, U.S. National Library of Medicine. Rockville Pike, Bethesda. USA. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7142975/ The Open Group (2011a). Architecture Development Method. The Open Group. USA. Reviewed in February 2018, http://pubs.opengroup.org/architecture/togaf9-doc/arch/chap05.html. Sankaralingam, K., Ferris, M., Nowatzki, T., Estan, C., Wood, D., & Vaish, N. (2013). Optimization and Mathematical Modeling in Computer Architecture. Morgan & Claypool Publishers. Scharre, P. & Riikonen, A. (2020). Defense Technology Strategy. Center for a New American Security. USA. The Open Group (2011b). Security Architecture and the ADM. The Open Group. https://pubs.opengroup.org/architecture/togaf91-doc/arch/chap21.html Stupples, B., & Sazonov, A., & Woolley, S. (2019). UBS Whistle-Blower Hunts Trillions Hidden in Treasure Isles. Bloomberg-Economics. Bloomberg. Reviewed in November 2019 https://www.bloomberg.com/news/articles/2019-07-26/ubs-whistle-blower-hunts-trillions-hidden-in-treasure-islands Tolos, J. (2018). Artificial intelligence-An introduction to AI. Meduim. https://medium.com/@joantolos/artificial-intelligence-abe8f1b1cfc2 Trad, A., & Kalpić, D. (2020a). Using Applied Mathematical Models for Business Transformation. IGI Complete Author Book. IGI Global. USA. Verdict (2020). Analysis-vActivity: Technology Trends GlobalData Thematic Research. Verdict. https://www.verdict.co.uk/vActivity-technology-trends/ Wooden, A. (2021). How BGI4VR is Revolutionising the Future of vActivity. Intel Corporation. https://www.intel.co.uk/content/www/uk/en/it-management/cloud-analytic-hub/big-data-powered-vActivity.html https://ijhem.com/cdn/article_file/2022-02-28-21-02-01-PM.pdf Antoine Trad Transformation Projects Virtual Reality Strategy Enterprise Architecture Geopolitics and Holism Security Artificial Intelligence Manager's Profile Enterprise Architecture Critical Success Factor 5143 51 3

89 https://ijhem.com/details&cid=89 2022-02-28 This paper illuminates how introducing cannabis on a college campus parallels the transition of cannabis in U.S. society moving from legitimate to illegal to legalization to corporate to academia. Using a case study methodology, the purpose of this research is to examine how a college or university might respond to a new industry opportunity. In response to a campus charge, student demand and industry demand, a small college located in the heart of Brooklyn, New York City answered a call to advocate on behalf of its student- and community members. Over a period of two-years, a new cannabis education and programs initiative was introduced to the campus within the backdrop of such actions being viewed as controversial. Introduction and approval of cannabis education on a campus required critical campus stakeholders to undergo change-shaping events over time that led to shifts in their attitudinal thinking. Throughout the two-year period, new courses were co-created by campus faculty and leading cannabis voices in the U.S. that included industry, investors, academics, and alumni who had accumulated cannabis expertise. The newly created rigorous and science-heavy curriculum spanned multiple academic departments and offering cross-listed courses, certificates, scholarships, industry-academic research, entrepreneurial assistance, various types of advocacies, and internship and employment pipelines. This study contributes to the body of higher education literature by mapping out steps institutions of higher learning might take to garner broad campus support for cannabis education Arcview Market Report (2019). Arcview Group, Intrado GlobalNewswire. Adinoff, B., & Reiman, A. (2019). Implementing social justice in the transition from illicit to legal cannabis. The American journal of drug and alcohol abuse, 45(6), pp. 673-688. Ayers, J. (2019) The need for federal regulation of marijuana marketing. JAMA. 321(22), pp. 2163–2164. Barcott, B., Whitnet, B., & Bailey, J. (2021). Legal cannabis now supports 321,000 full-time American jobs. Jobs Report 2021. pp 1-16. Black, B. (2020) Creating a qualified cannabis workforce: How higher education can support cannabis career pathway. 9th International Conference on Business & Economic Development. Online. August 22, 2020. Bodwitch, H., Carah, J., Daane, K. M., Getz, C., Grantham, T. E., Hickey, G. M., & Wilson, H. (2019). Growers say cannabis legalization excludes small growers, supports illicit markets, undermines local economies. California Agriculture, 73(3), 177–183. Bunting, W.C., Garcia, L., & Edwards, E. (2013) The war on marijuana in black and white. American Civil Liberties Union, June, pp. 1-190. Caligiuri, F.J., Ulrich, E.E., & Welter, K.J. (2018). Pharmacy student knowledge, confidence and attitudes toward medical cannabis and curricular coverage. American Journal of Pharmaceutical Education, 82(5), pp. 424-432. Cheng, C., Mayer, W.J., & Mayer, Y. (2018). The effect of legalizing retail marijuana on housing values: Evidence from Colorado. Economic Inquiry, 56(3), pp. 1585-1601. Collins, J. (2020). A Brief History of Cannabis and the Drug Conventions. AJIL Unbound, 114, pp. 279-284. Coopman, S. & Coopman, T. (2020). ExperientialLearning@SocialMedia.edu: Using the tech start-up concept to train, engage, and inform students. Ubiquitous Learning: An International Journal. 13, pp. 1-29. Corso, R. (2020) Building an Innovative and Entrepreneurial Dimension in an Institution of Higher Education. Higher Education for the Future, 7(2), pp. 200-214 Crowder, L. (2019). The rapid evolution of UC cannabis research. California agriculture 73(3-4), 106-112.R-15. Crump, M.E.C., Rolle, J.D., Reid, A.E., & Brevett, R. (2020). Faculty impact on campus survivability through student experience: An appeal to reengineer faculty offerings to students at at-risk colleges facing existential threat. Paper presented at the 9th International Conference on Business & Economic Development, online. August 22, 2020. Crump, M.E.S., Hills, N.J., & Hardin, B.W. (2018).  Informal economy as a cause for understating actual rates of entrepreneurship among blacks in the U.S. and in Africa. International Journal of Business & Economic Development, 6(3), 15-28. Crump, M.E.S., Singh, R.P, Wilbon, A.D., & Gibbs, S.R. (2015). Socio-demographic differences of black versus white entrepreneurs. Academy of Entrepreneurship Journal, 21(1), pp. 115-129. Crump, M.E.S. (2013).  Black entrepreneurship: Formal versus informal economy exploitation.  Washington Business Research Journal, 3(1), pp. 85-111. DiDiodato, G., Hassan, S., & Cooley, K. (2021). Elicitation of stakeholder viewpoints about medical cannabis research for pain management in critically ill ventilated patients: A Q-methodology study. PLoS ONE, 16(3). pp. 1-11. Dills, A. K., Goffard, S., Miron, J., & Partin, E. (2021). The Effect of State Marijuana Legalizations: 2021 Update. Cato Institute, Policy Analysis, 908, pp. 1-40. Erickson, B (2020). Cannabis research stalled by federal inaction. US scientists face numerous barriers to studying health effects of cannabis. C&EN 98(25), pp. 2-10. Ferraiolo, K. (2007) From Killer Weed to Popular Medicine: The Evolution of American Drug Control Policy, 1937–2000. Journal of Policy History, 19(2), pp. 147 - 179. Fromhold-Eisebith, M. & Werker, C. (2013). Universities’ functions in knowledge transfers: A geographical perspective. The Annals of Regional Science, 51, pp. 621-643. Ghiabi, M., Maarefvand, M., Bahari, H., & Alavi, V. (2018). Islam and cannabis:  Legalisation and religious debate in Iran. International Journal of Drug Policy, 56, pp. 121–127. Grech, V. (2019). Towards evidence-based medical cannabis. Malta Medical Journal 31(2), pp. 3-8. Grey, S. (2016). Cannabis and spirituality. Park Street Press, Rochester, Vermot. Hasse, J. (2019,). U.S. Universities offering cannabis – focused graduate programs and masters. Forbes, July 17. Harris, K., Matin, W. (2021). Persistent Inequities in Cannabis Policy. The Judges' journal. 60(1), pp. 9-13. HMS Communications (2019). $9 million donation earmarked for cannabis research. The Harvard Gazette. Hill, K.P., & Palastro, M.D. (2017). Medical cannabis for the treatment of chronic pain and other disorders: misconceptions and facts. Poli Arch Intern Med, pp. 1-5 Hillerbrand, R., & Werker, C. (2019). Values in University–Industry Collaborations: The Case of Academics Working at Universities of Technology. Science & Engineering Ethics, 25(6), pp. 1633–1656. Ipsos, (2020) Universities: Perceptions impacts and benefits – Higher Education around the world. Fulbright Commission, University of California and King’s College London, Survey. Kilmer, B. (2019). How will cannabis legalization affect health, safety, and social equity outcomes? It largely depends on the 14 Ps. American Journal of Drug & Alcohol Abuse, 45(6). pp. 664–672. Kedia, B. L., Clampit, J., & Gaffney, N. (2014). Globalizing Historically Black Business Schools: A Case Study of the Application of Modern Pedagogical Theories of Internationalizing Higher Education. Journal of Teaching in International Business, 25(3), 214–234. Kruger, D., Kruger, J.S., Bednarczyk, E.M., & Prescott Jr., W.A. (2021) Cannabis education in United States Pharmacy Colleges and Schools Journal of the American College of Clinical Pharmacy. Advances in Clinical Pharmacy Education & Training. Lashley, K., & Pollock, T. G. (2020). Waiting to Inhale: Reducing Stigma in the Medical Cannabis Industry. Administrative Science Quarterly, 65(2), 434–482. Lee, M. J., Rittschof, C. C., Greenlee, A. J., Turi, K. N., Rodriguez-Zas, S. L., Robinson, G. E., Cole, S. W., & Mendenhall, R. (2021). Transcriptomic analyses of black women in neighborhoods with high levels of violence. Psych neuroendocrinology, 127, pp. 1-8. Mishra, S. (2020) Social networks, social capital, social support, and academic success in higher education: A systematic review with a special focus on ‘underrepresented’ students. Educational Research Review 29(100301), pp. 1-24. Musto, D.F. (1991). Opium, cocaine, and marijuana in American history. Scientific American 265(1), pp. 20–27. OECD. (2020, June 18). Resourcing higher education: Challenges, choices, and consequences. OECD Report, pp. 1-168. Parker, S. (2020). The future of higher education in a disruptive world. KPMG International Report, pp. 1-30. Prigge, G. W. (2005). University-Industry partnerships: What do they mean to universities? A review of the literature. Industry and Higher Education, 19(3). pp. 221-229. Rankin, J., Garrett, R., & MacGill, B., (2021). Critical encounters: Enacting social justice through creative and body-based learning.” Australian Educational Researcher, 48(2), pp. 281–302 Rahwanji, M. (2019). Hash’ing out inequality in the legal recreational cannabis industry. Northwestern Journal of International Law & Business 39(3), pp. 333–357. Schwartz, J. (2020). Work disrupted: Opportunity, resilience, and growth in the accelerated future of work. Wiley, New Jersey, USA. Singh, R.P., Knox, E.L., & Crump, M.E.S. (2008).  Opportunity recognition differences between black and white nascent entrepreneurs:  A test of Bhave’s model.  Journal of Developmental Entrepreneurship, 13(1), pp. 59-75. Taneja, H. (2018). Unscaled: How AI and a new generation of upstarts are creating the economy of the future. Clays Ltd. St Ives plc, Great Britain. Terry, C.E. (1915). The Harrison Anti-Narcotic Act. American Journal of Public Health 5(6), pp. 518. Tholen, G, Relly, S.J., Warhurst, C., & Commander, J. (2016). Competency development in business graduates: An industry-driven approach for examining the alignment of undergraduate business education with industry requirements. British Educational Research Journal, 42(3), pp. 508-523. Vannabouathong, C., Zhu, M., Chang, Y., & Bhandari, M. (2021). Can medical cannabis therapies be cost-effective in the non-surgical management of chronic knee pain? Clinical Medicine Insights: Arthritis & Musculoskeletal Disorders, 14, pp. 1-10. Vitiello, M. (2021). The war on drugs: moral panic and excessive sentences. Cleveland State Law Review, 69(2), pp. 441–483. Watenberg, A. (2021) Cannabis and the Environment: What Science Tells Us and What We Still Need to Know. Environ. Sci. Technol. Lett. 8 (2), 98–107 Zolotova, Y. (2021) Medical cannabis education among healthcare trainees: A scoping review. Complementary Therapies in Medicine, 58, (102675), pp. 1-8. https://ijhem.com/cdn/article_file/2022-02-28-21-07-19-PM.pdf Alicia E. Reid Micah E. S. Crump Vikiana R. Clement JoAnn D. Rolle Cannabis education College campus Medgar Evers College Social equity Legislating cannabis Cannabis science Cannabis research 5173 55 0

90 https://ijhem.com/details&cid=90 2022-02-28 In this paper the alignment of computer and digital literacy as well as student academic success were examined. Lack of adequate functional digital literacy training and the unreadiness of higher education institutions for the impact of random shocks such as COVID-19 pandemic has gravely affected teaching and learning. The purpose of this paper is to make the case for preparedness of students to meet the needs of technology jobs by mandating and aligning digital literacy and student success. As COVID-19 Pandemic was spreading through the communities of the United States, institutions of higher education transitioned to fully online teaching and learning. Prior to the pandemic, fully online education was secondary to face-to-face format. Only about 20% of classes were fully online while 80% were face-to-face. Digital literacy was given a token treatment as a percentage of the entire curriculum and relegated to only the departments of computer information systems and computer sciences. Faculty, students, staff, families, and communities were not trained for the intensity of fully online education as the only format. Many of them never heard of most of the digital literacy tools. Both faculty and students were forced to learn the use of computers and digital literacy tools to survive the spring 2020 semester. Low-income families were left to the operational schedules of local libraries, many of whom did not have internet presence. The institutions provided limited training for faculty and students to meet the urgency of the time. Faculty and students were forced to purchase expensive tools and hardware to withstand the intensive demand of teaching and learning. Many students were overwhelmed by the pressure of the new way of learning; some dropped out of school while many performed very poorly in their examinations which negatively impacted their overall grade point averages.  One year later in spring 2021, the student success outcomes have barely changed. At the same time, technology is advancing despite the raging COVID pandemic. Millions of technology-enabled jobs remain unfilled while millions of university graduates are unemployed. There continues to be a mismatch between current job requirements in the industries and graduating students’ skills. This paper discusses the indispensable value of building computer and digital literacy training into all undergraduate curriculums. We argue for mandated computer and digital literacy exit skills assessment test for all graduating students irrespective of their discipline. We also make the case for increased institutional investment in faculty training in computer and digital literacy readiness. There are a number of remedies suggested to address the speed of advancement in technology, faculty and student functional mastery of basic computer and digital literacy skills. We conclude that all institutions must be proactive rather than reactive to systemic shocks by preparing students for academic success and technological readiness for today’s job markets Affouneh, S., Salha, S., N., & Khlaif, Z. (2020). Designing quality e-learning environments for emergency remote teaching in coronavirus crisis.      Interdisciplinary Journal of Virtual Learning in Medical Sciences, 11(2), 1–3 Anderson, Allison. 2020. COVID-19 Outbreak Highlights Critical Gaps in School               Emergency Preparedness, The Brookings Institution March 11, 2020. Azzi-Hucktigran Kaliope, and Tigran Shmis. 2020. Managing the Impact of COVID-19 on Education Systems Around the World: How Countries Are Preparing, Coping, and Planning for Recovery, The World Bank, March 18, 2020. Basilaia, G., Kvavadze, D. (2020). Transition to online education in schools during a       SARS-CoV-2 coronavirus (COVID-19) pandemic in Georgia. Pedagogical Research,     5(4), 10. https://doi.org/10.29333/pr/7937 Google Scholar Brianna, D., Derrian, R., Hunter, H., Kerra, B., & Nancy, C. (2019). Using EdTech to enhance learning. International Journal of the Whole Child, 4(2), 57–63. Casey, L., Bruce, B.C. (2010) Sustaining the Inquiry Cycle: Digital literacy reframed, in Gibson, D., Dodge, B. (Eds) Proceedings of Society for Information Technology & Teacher Education International Conference 2010, pp. 1372–1379. Chesapeake,        VA: AACE.  http://www.editlib.org/p/33548; http://www.editlib.org/p/33548 Cluver L. Parenting in a time of COVID-19. Lancet. 2020;395(2020): E64. [PMC free article] [PubMed] [Google Scholar] Dhawan, S. (2020). Online learning: A panacea in the time of COVID-19 crises. Journal of              Educational Technology, 49(1), 5–22. https://doi.org/10.1177/0047239520934018 Doucet, A., Netolicky, D., Timmers, K., Tuscano, F. J. (2020). Thinking about pedagogy in an unfolding pandemic (An Independent Report on Approaches to Distance       Learning during COVID-19 School Closure). Work of Education International and UNESCO.  https://issuu.com/educationinternational/docs/2020_research_covid                 -19_eng. Google Scholar Favale, T., Soro, F., Trevisan, M., Drago, I., & Mellia, M. (2020). Campus traffic and eLearning during COVID-19 pandemic. Computer Networks, 176, 107290. García, Emma, Elaine Weiss, and Lora Engdahl. 2020. “Access to Online Learning Amid                 Coronavirus Is Far from Universal, and Children Who Are Poor Suffer from a Digital Divide.” Working Economics Blog (Economic Policy Institute), April 17,        2020. Greeley, Christopher S. 2020. “Child Maltreatment Prevention in the Era of Coronavirus              Disease 2019.” JAMA Pediatrics. Published online August 03, 2020.             https://doi.org/10.1001/ jamapediatrics.2020.2776. Hodges, C.B., Moore, S.L., Lockee, B.B., Trust, T., Bond, M.A. (2020, March 27). The difference between emergency remote teaching and online learning. EDUCAUSE Review. https://tinyurl.com/rekxcrq Lazarus, J. V. et al. COVID-SCORE: a global survey to assess public perceptions of government responses to COVID-19 (COVID-SCORE-10). PLoS ONE 15,         e0240011 (2020). Mantiri, O., Hibbert, G. K., & Jacobs, J. (2019). Digital literacy in ESL classroom. Universal Journal of Education Research 7(5), 1301-1305. Retrieved from                http://www.hrpub.org DOI: 10.13189/ujer.2019.070515 Murgatrotd, S. (2020, March). COVID-19 and Online learning, Alberta, Canada.                doi:10.13140/RG.2.2.31132.8512. Google Scholar Petrie (2020), “Covid-19 and the Australian Human Rights Acts. https://orcid.org/0000-0003-1061-2863 Pew Research Center, 2021, “Social Media Use in 2021”, Survey of US Adults Conducted January 25 – February 8, 2021 Sintema, E. J. (2020 April 7). Effect of COVID-19 on the performance of grade 12             students: Implications for STEM education. EURASIA Journal of Mathematics, cience and Technology Education, 16(7).  https://doi.org/10.29333/ejmste/7893. Google Scholar Stratford, Brandon. 2020. As Schools Reopen, Addressing COVID-19–Related Trauma and Mental Health Issues Will Take More Than Mental Health Services. Child       Trends, July 2020. Tinubu Ali, Titilayo, and Mirel Herrera. 2020. Distance Learning During COVID-19: 7 Equity Considerations for Schools and Districts. Southern Education Foundation,          April 2020. UNESCO. Education: From Disruption to Recovery. (2020). Available online at:  https://en.unesco.org/covid19/educationresponse (accessed May 24, 2020).          United States Federal Communications Commission (FCC), 2014 Widana, I. (2020). The effect of digital literacy on the ability of teachers to develop HOTS-based assessment. Journal of Physics: Conference Series. 1503. 012045. 10.1088/1742-6596/1503/1/012045 https://ijhem.com/cdn/article_file/2022-02-28-21-34-18-PM.pdf Veronica Udeogalanya Academic Success Aligning COVID-19 Digital Literacy Student Pandemic 6996 89 22