Artificial Intelligence in Higher Education


For the past five years, different sectors have progressively realized the essence of the emerging technologies. Some of the emerging technologies include blockchain technology, data science, Artificial Intelligence, neural networks, machine learning, Internet of Things and Big Data. A keen focus to Artificial Intelligence has led to realization of human intelligence processes by computer systems or machines. The process behind Artificial Intelligence (AI) constitutes acquisition of the information and rules, self-correction and reasoning. Significant applications of artificial intelligence are not limited to machine vision, expert systems, as well as speech recognition. AI, in some of the sectors, is offered as a service with companies and individuals given a chance to experiment it for different business purposes. Some of the offers include Google AI, Amazon AI, Microsoft cognitive services, and IBM Watson Assistant.


Further observations made on a global scale indicate that Artificial Intelligence has already made its way in higher education, especially in private and public universities. Popenici and Kerr (2017) explored phenomena around the use of AI in teaching as well as learning across higher education. The authors argued advancement in higher education is essentially attached to development of computing capacities and new technologies as far as new intelligent machines are put into consideration. This means that AI is opening new possibilities as well as challenges encountered during learning and teaching. The impact of such a move rests with fundamental changes that occur in terms of internal architecture and governance. Popenici and Kerr (2017) registered that Alan Turing, in 1950s, proposed a significant solution when designing a system while regarding human as intelligent. He suggested an imitation game that tests the capacity of the human listener while drawing distinction between conversation made with another human or the one made with a machine. Since the year 1956, a series of theoretical understanding of AI have been fronted through mathematics, advanced AI solutions, chemistry, linguistics, and biology. Notably, most of the contributions made have only attracted a series of disputes with most of them presenting limited perspectives.

Despite the disputes and disagreements towards AI and its applications, AI is developing and applied at an accelerated pace. This means that it carries with it the influence that influences the nature of services across higher education. IBM’s supercomputer Watson has attracted the incipient form of AI, which is commonly applied in universities. The solution has been applicable in Deakin University in Australia where AI was effective in delivering students’ advice throughout the year. The case Deakin signifies the future of AI especially when deployed for administrative duties. Badran et al. (2019) asserted that the new administrative role entrusted to AI is likely to change the structure as well as quality of services. The same change can equally apply to the structure of the workforce and the entire university. On the other hand, a supercomputer can be thought to bespoke feedback at any possible time while reducing employment of administrative staff used to serve the same function. Machine learning can equally impact the same change given that it also depends on programming, which is the same case when it comes to AI solutions.

With the vast focus on machine learning and artificial intelligence, studies have still introduced research gaps, which need more attention. In a country like Saudi Arabia, AI has just started making its roots with rare implementation done in universities. Based on this, the research aims at establishing the possible impact of AI on teaching and learning across both private and public universities based on the stakeholder’s point of view. This attracts the following research questions.

Do people understand what AI is and its application in the education sector?

Does AI carry with it the achievement seen in the education sector?

Do universities in Saudi Arabia regard AI as technology that would attract reforms?

Does AI have any future in the world of disruptive technologies?

Data collection

A research on AI and its impact on sectors, including education, is one of the most challenging areas due to the limited applications as seen on a global scale. By far, developed countries have an upper hand of affording the latest technology in the market, which is a case that rarely happens in developing nations. Therefore, subjects pertaining technology are somehow sensitive especially when it comes to applications. This is the same precaution the research takes while constructing the methodology and more so, the critical area of data collection. The research takes into consideration the mixed methods as the ultimate tool that would probe into primary sources of data before reaching the conclusion. The method strikes an integration of qualitative and qualitative research methods while seeking their collective advantages. The prime advantage of this integration lies behind the permission to forge a more complete as well as synergistic utilization of the collected data. The adoption of mixed methods is believed to attract the use of qualitative data in exploring the quantitative findings. This also paves way for development of survey instruments. Based on the framework of mixed methods, the research will absolutely rely on primary research data as the tool for data collection.

Under primary research, the research will do both the questionnaire survey and an interview. The research will first conduct the questionnaire survey in 3 private universities and 3 public universities to determine the impact of AI in teaching and learning. The approach will essentially be used on staff members in the relevant faculties for the purposes of tapping into tangible details. The questionnaire would attract more closed ended questions, which compels the research to integrate the likert scale. The survey is anticipated to attract at least 150 respondents from the 6 universities, which implies that each university has to give at least 25 participants. Thereafter, the research will focus on an interview, which shall be seeking insights regarding the research questions tapped or modified from the questionnaire. The interview is expected to attract 36 participants with each university expected to give 6 responds. A reduction in number of participants from 150 to 36 as the research moves to questionnaire to interview is based on the fact that interviews are likely to take more time. This means that one can only handle a limited number of interviewees within the allocated time by the relevant authorities within the institution.

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Data Analysis

The research would have to conduct data analysis for it to arrive at justifiable conclusions. It is worth noting that two analytical tools would be put into use. First, the research will conduct factor analysis due to the quantitative side of the research. Factor analysis is largely seen as a statistical method applicable in describing variability across the correlated variables. The research will check on two important aspects in the data collected. These include factor loading and uniqueness. Factor loadings will check on the commonality across the square of the standardized outer loading while uniqueness will confirm the variability of any given variable without communality. Thematic analysis is the second tool that will be put into use and it focuses more on qualitative data collected through interviews. The tool shall focus on identifying, interpretation, as well as analysis of patterns across the quantitative data. All stages of thematic analysis shall be observed and used in the required for the purposes of drawing conclusions as regards aspects of AI, and justify the findings extracted from the quantitative research. The research will first familiarize with data through reading as well as re-reading the recorded scripts. This will prompt generation of initial codes while attracting small chunks of data. Afterwards, the research will search for themes believed to capture significant as well as interesting side of the captured data. Subsequent stages demand a review of themes before defining theme. The definition of themes will also be aligned to what factor loading would have provided at the time.

Potential Challenges and ethics

While fronting this proposal, it is significant to point out possible issues that can emerge in the course of data collection and during analysis. Techniques used in data collection include the questionnaire survey and an interview. In both cases, the research will directly interact with participants. Issues expected from these processes include discipline, simplicity, permissions, costs and time allocation. All these need to be reviewed and significant solutions worked on to avoid problems on the eve of the research. The critical side of this research constitutes the ethics that need to be observed. Such ethics include the issue of informed consent, privacy and confidentiality of the participants. The research will adhere to the university guidelines and consult widely before attaining or addressing the informed consent from participants. First, the research will share the participant information sheet which provides a review of the research details. The information includes the dates, what is expected from the researcher, what is expected from the participant, the kind of questions expected in the research and the rights assigned to the participant. The participant information sheet shall be followed by informed consent forms, which requests the participant to declare his or her knowledge of the research and express willingness to take part. Issues of privacy and confidentiality are critical but need to be addressed before, during and even after the research. Notably, a pilot study will be conducted to establish possible problems that are likely to emerge.

Situating with existing literature

The project taps into a vast range of studies linked to the topic under study while drawing a difference in establishing the research gap. For instance, Sheldon et al. (2008) assert that the future of education largely lies behind technology, and specifically Artificial Intelligence. While other technologies are equally changing or influencing the education landscape, artificial intelligence has gained more attention than what one would have expected of it. Saudi Arabia has already made significant moves in embracing artificial intelligence as one of the emerging solutions in business and education sector. This can be noted through establishment of the Artificial Intelligence Centre, which has essentially shaped the pursuit of the technology and its implementation in higher education. AI can best be perceived as an assistive technology that can convert speech to text, text to speech, provide the zoom capacity, predict the text, enable search engine and spell checking. The mentioned roles are yet to be felt by Saudi Arabian private and public universities. This attracts a research gap on how the stakeholders feel towards the impact of AI especially towards teaching and learning across higher education spectrum. Perhaps, this research is based on the assumption of awareness of AI among the stakeholders despite lack of its use in universities. This is a new area of research and it narrows down to primary methods for the purposes of finding out the preparedness and speculated benefits of AI in private and public universities.

In Saudi Arabia, Jewell (2018) noted that AI is slowly making its way in the education sector. The country is said to undertake the most ambitious as well as largest economic transformation and reform in the entire history. Ranges of the relevant initiatives are in the pipeline in the course of realizing vision 2030. In Saudi Arabia, artificial intelligence as well as digitization efforts are thought to be the enablers of the wide ranging reforms. According to Jewell (2018), the emergence of AI and its planned implementation can perfectly be aligned to digital capabilities and other technologies such as Blockchain and Internet of Things among others. This happened in pursuit of solutions as well as services that are likely to emerge in the course of the Vision 2030 transformation. The enormous undertaking is likely to touch major educational reform, which is expected to impart digital skills among students. Alongside the artificial intelligence, Saudi Arabia has shown its ultimate commitment towards building sustainable cities as well as communities, enhance the quality of education, foster innovation and economic growth as well as take care of the wellbeing and health of citizens. However, Shehabat and Mahdi (2009) indicate that the Arab world has been lagging behind in terms of embracing artificial intelligence. In this sense, Saudi Arabia recently counted AI as part of the innovative moves under ICT that shall promote sister agencies within government as well as the private sector. For instance, the education sector in Saudi Arabia is paying more attention towards innovation labs where entrepreneurs together with students can explore new ideas. Such efforts are what define the course of this research with more insights being extracted from a series of studies.

How does this project further existing knowledge?

AI is not a new field because a number of studies have already touched on its impact, the constituents, its future, and its vast application. This project furthers the existing knowledge through a number of ways. First, the research takes into account ideas and concepts linked to AI while tapping into its history before linking it to the education landscape. This means that most of concepts can be drawn from other sectors like business before establishing a trend that would tap into the influence AI has on education. Secondly, the project will tap into academic literature attached to the impact AI has on higher education as far as parameter of teaching and learning are put into consideration. The ideas, concepts, and arguments extracted from literature will still find their way in this research while trying to draw comparisons and seeking theoretical justification where necessary. Finally, the research considers primary sources, which facilitates the original understanding of the research aim and objectives. This understanding is a true reflection of what is happening around the world based on societal perception and trends.

Details of any previous work in the proposed field

Prior experience may be of great help in handling this project. My knowhow in AI applications has been bolstered by the research I did before on emerging technological solutions in the modern world. However, this was a personal initiative and an interest in the commonly discussed technology such as Internet of things, big data, Blockchain, data analytics, and artificial intelligence among others.


The proposal presents one of the most critical subjects regarding the influence of technology where it has never been implemented. It has attracted new interest in Artificial Intelligence and the impact it is likely to create towards teaching and learning across private and public universities. The proposal has provided a background on AI and the move by Saudi Arabia in embracing it. It could be noted that AI has not yet made its way to Saudi Arabian universities. The proposal also points out the used of mixed methods as an umbrella that covers data collection tools. The questionnaire survey and interview have been chosen as the tools for collecting data while factor analysis and thematic analysis shall be used for data analysis. The proposal has also indicated concerns of extending the existing knowledge and also the relevance of the project to prior works.


Sheldon, P., Fesenmaier, D., Woeber, K., Cooper, C. and Antonioli, M., 2008. Tourism education futures, 2010–2030: Building the capacity to lead. Journal of Teaching in Travel & Tourism, 7(3), pp.61-68.

Badran, A., Baydoun, E. and Hillman, J.R. eds., 2019. Major challenges facing higher education in the Arab world: Quality assurance and relevance. Springer.

Popenici, S.A. and Kerr, S., 2017. Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), p.22.

Shehabat, I.M. and Mahdi, S.A., 2009, April. E-Learning and its Impact to the Educational System in the Arab World. In 2009 International Conference on Information Management and Engineering (pp. 220-225). IEEE.

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