Academic and Administrative Role of AI in Education: History
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Contributor:
Muhammad Alam
,
Sayed Ahmad
,
Mohd Khairil Rahmat
,
Muhammad Shujaat Mubarik
,

Using AI in education can have a dramatic impact on the way academic and administrative staff use their time and the manner in which students are served individually. Artificial Intelligence Applications are assisting the education sector organizations at two main levels.
1. Administrative level (admission, counseling, library services, etc.)
2. Academic Level (assessment, feedback, tutoring, etc.)

  • Artificial Intelligence Applications (AIA)
  • Personalized Education (PE)

1. Artificial Intelligence Applications in Grading/Assessment

Assessment of a student means collecting, analyzing information, interpreting, and acting on that information about his/her performance with respect to learning goals [1]. There are many types of assessments. However, the choice of assessment depends upon the purpose and choice of the person making the assessment. For example, educational institutes mainly use standard-based assessments [2] which is beneficial for grading, etc. Another type of assessment is learner-centered measurement models [3] which are most formative and is beneficial for the guidance of instruction and for the supporting learning of students. It may or may not always be valid or useful. Unlike the traditional or old methods of assessments, currently, computer-based applications are also in use for the purpose of assessments [4]. Those AI applications not only give a rapid assessment of large numbers of students but also same the same standards without any biases, etc., and all students get the grading without the fear of bias from any likes or dislikes. Additionally, it also assists the teachers, minimizes their workload, and provides time for other tasks.
On one hand, the assessment of a large number of students is not an easy task, on the other hand, it is one of the primary tasks of teachers [5]. After the attack of COVID-19, educational institutes have shifted their operation to online learning systems and systems like Learning Management Systems, MOOC, MOODLES, etc., and it is very difficult for teachers to handle everything online, especially the assessment of assignments, quizzes, and answer papers [6]. Automatic assessment or grading systems are one of the answers to address the issue. Questions of various types like short answer questions, multiple-choice questions, etc., can be assessed through automatic assessment systems [7]. Many researchers have worked for the development of impartial and effective grading or assessment systems using different types of computer technology [8][9]. AI applications through machine learning methods and unsupervised clustering algorithms can work effectively and can address the challenge [10]. The scope of this study is limited to the managerially highlight the use of AI applications in the assessment of student performance, the technicalities of which are described by [11][12][13], etc. It can be concluded that AI applications can assist a lot in the student performance assessment and grading as well as minimize the tasks of a teacher. This will also increase impartiality and effectiveness in assessment as compared to the traditional type of assessment.

2. Artificial Intelligence Applications in Admission

As AI has its helping characteristics in many walks of life especially in education, its application starts from the admission process. Many education institutes advertise admissions on their websites and the students expect satisfactory services like consultation and information related to admission [14]. Nowadays, many universities, etc., are providing the necessary services to be given to the candidates or their parents, through web-based service systems. Therefore, this requires it to be developed as user-friendly as possible, which is the main factor innovators consider during the technology acceptance model [15][16][17]. Although websites provide a lot of help in the admission process, it may lead to a pool of questionnaires and lengthier waiting times which minimizes visitor satisfaction [18]. Like humans, AI can also answer questions and provide information. One of the means which is very famous is Chatbot, where computer-based information technology system interacts with humans [19]. It is defined as a “software program that simulates a conversation with human users, using text, voice, or images or a combination of spoken and visual heuristics” [20]. It is used in many commercial and education universities’ websites to answer visitors like a human would [21].
Chatbots are referred as technology-fueled virtual assistants and it stems from established/written scripts or AI. It works 24/7 by providing the necessary knowledge and answers related to admission. It not only helps the visitors or information seekers around the clock to obtain what they need but also reduces the burden on the admission staff, etc. [22]. This study is not looking into the technical structure or algorithm of Chatbot, it only focuses on its usage in the admission process and how it reduces the workload on the admission department. However, it is important to be mentioned here in layman language that it uses a keyword or string similarity algorithm to search through a script or data set and finds a suitable answer. In addition, it also provides relevant links to the user if he/she is not satisfied and a web interface for both the user and the administrator.
It can be summarized from the literature that AI applications in the form of Chatbot, etc., are helping the education institutions in the admission process to a greater extent. It not only assists in the admission process but also decreases the question answering burden on admission staff and department. It provides services round the clock without any assistance from humans. Figure 1 shows the detailed structure of Chatbot and how it works [23].
Figure 1. Detailed structure of Chatbot.

3. Artificial Intelligence Applications as Virtual Reality (VR)

Virtual reality technology has appealed to many sectors, especially education by providing and adding new opportunities and benefits to the education process [24]. It is a stimulating experience similar to the real world or as desired by the programmer. Its applications extend to education, business, and entertainment [25]. It is also defined as “the computer-supported setting that enhances the real-world experience through the provision of multi-aesthetic stimuli (e.g., visual, audio, motion)” [26]. Examples of VR settings used in educational institutions are room-scale VR such as CAVE, standalone-VR such as Oculus Rift, HTC Vive, and mobile-VR such as Samsung Gear VR, Google Cardboard, etc. With the expansion of education around the globe, it is necessary to provide well-designed instructional contexts to learners or students, and the name that comes to mind for the purpose is virtual reality. Through it, science, engineering, technology, mathematics, physics, etc. education can be delivered in an outstanding manner [27].
Virtual reality can address the issues that teachers and students face in traditional instructional methods like delivering lectures in classrooms and conducting experiments physically, where acquiring in-depth knowledge and understanding are very important [28]. VR is contributing to the learning process in a potential manner due to its value addition to interactivity, information intensity, and involvement [29]. Some experiments are very risky and there are a lot of environmental and safety concerns, some activities like field-based experiments and the use of expensive equipment are very costly; for both cases, VR is the best option. Another case of VR usage and its importance is where when the students or learners have no experience of using something or of working in an environment and safety and security risks are there [30]. Even if a teacher is present in such critical situations, it is not possible for him/her to provide enough attention and time to each student, which further creates frustration, negative emotions, dissatisfaction, etc., among the students. With such feelings, students can neither learn the theoretical knowledge nor obtain experiential advancement [31].
However, in learning through VR, the learners are free from any interruptions and can get a high-level involvement in the environment. The environment provides a sense of realism to the learner which leads to the sense of presence and finally to learning outcomes in a positive manner [32][33].
In short, VR is used to provide education with the help of a virtual environment where learners or students develop and enhance their understanding, skills, and experience without facing the fear of failure, danger, or any other negative consequences. It is in used in school education [34], military training [35], medical education [36], astronaut training [37], miner training [38], driver training [39], civil engineering [40], etc.

4. Artificial Intelligence Applications in Learning Analytics

Learning Analytics (LA) is defined as “the measurement, collection, analysis, and reporting of data about learners and their contexts, for the purposes of understanding and optimizing learning and the environments in which it occurs” [41]. It has attracted the eyes of many areas like academics, research, etc. In education, especially, this is due to the need of getting a better understanding of teaching, personalization, adaptation, and intelligent content. After the emergence of the big data concept, analytics has the capabilities to increase the productivity of an organization [42] and enhance competition [43]. It is a fact that the education sector has not used the data for improvement but the development of AI and its application in education has motivated the educators to collect and analyze the data and put solutions to many issues and challenges. Nowadays data from education institutes are used for making various types of analyses and decisions [44]. The data is collected and recorded when the students use social media, LMS, MOOC, etc. Their clicks on various buttons, navigation, the time they spent on a task, everything can be tracked which then is used by the analysts to evaluate the teaching environment and enhance it [45]. This is evident from another definition which is “Analytics is the process of developing actionable insights through problem definition and the application of statistical models and analysis against existing and/or simulated future data” [46]. It is concerned with making sense of data, action, and data mining in education institutes.
Learning Analytics is seen differently by different researchers. Perhaps the reason behind this is its applications to different areas and at different levels. It is viewed as a prediction model due to its use of intelligent data, the data produced by the learner, and the analytical models to predict student learning [47]. It is also considered a generic design framework [48], data-driven decision-making [49], an application of analytics [50], and the application of data science [51].
In the education and learning industry, analytics is important and necessary at different levels like classrooms, departments, universities, and the regional, national, and international levels. At each level, it gives different outputs for the betterment of education. For example, in classrooms, it gives information about the student’s interest, social networks, intelligence level, grades, and many more. At the department level, it gives the statistics about the department like risk, intervention, support services, and guides what to do and what not. At a national and international level, it gives direction for educational policy, budget, etc. It is termed as micro, meso, and macro-analytics level [52]. For each level, there is a need for different types of data sets, depending upon the objectives and contexts of the analytics. LA is explained in Figure 2 as proposed by Khalil and Ebner [53].
Figure 2. Khalil and Ebner’s Learning Analytics life cycle.

This entry is adapted from the peer-reviewed paper 10.3390/su14031101

References

  1. Redcay, E.; Schilbach, L. Using second-person neuroscience to elucidate the mechanisms of social interaction. Nat. Rev. Neurosci. 2019, 20, 495–505.
  2. Bennett, R.E. Formative assessment: A critical review. Assess. Educ. Princ. Policy Pract. 2011, 18, 5–25.
  3. Thahirah, A. Great schools partnership. Standards-based definition. In The Glossary of Education Reform; Great Schools Partnership: Portland, ME, USA, 2018.
  4. Rabin, E.; Kalman, Y.M.; Kalz, M. An empirical investigation of the antecedents of learner-centered outcome measures in MOOCs. Int. J. Educ. Technol. High. Educ. 2019, 16, 1–20.
  5. Al-Amri, S.; Ali, Z. Systematic review of computer based assessments in medical education. Saudi J. Med. Med Sci. 2016, 4, 79.
  6. Khan, R.A.; Jawaid, M. Technology enhanced assessment (TEA) in COVID 19 pandemic. Pak. J. Med Sci. 2020, 36, S108.
  7. Lorica, B. Semi-Automatic Method for Grading a Million Homework Assignments. Available online: http://radar.oreilly.com/2013/10/semi-automaticmethod-for-grading-a-million-homeworkassignments.html (accessed on 10 October 2013).
  8. Brew, C.; Leacock, C. Automated Short Answer Scoring. In Handbook of Automated Essay Evaluation: Current Applications and New Directions; Routledge: London, UK, 2013; p. 136.
  9. Liu, X.; Wang, S.; Wang, P.; Wu, D. Automatic grading of programming assignments: An approach based on formal semantics. In Proceedings of the 41st International Conference on Software Engineering, Software Engineering Education and Training, Montreal, QC, Canada, 25–31 May 2019; pp. 126–137.
  10. Morris, D. Automatic grading of student’s programming assignments: An interactive process and suite of programs. In Proceedings of the 33rd Annual Frontiers in Education, Westminster, CO, USA, 5–8 November 2003.
  11. Kowsari, K.; Jafari Meimandi, K.; Heidarysafa, M.; Mendu, S.; Barnes, L.; Brown, D. Text Classification Algorithms: A Survey. Information 2019, 10, 150.
  12. Devlin, J.; Chang, M.; Lee, K.; Toutanova, K. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv 2018, arXiv:1810.04805. Available online: https://arxiv.org/abs/1810.04805 (accessed on 14 March 2021).
  13. Goldberg, Y. A Primer on Neural Network Models for Natural Language Processing. J. Artif. Intell. Res. 2015, 57, 345–420.
  14. Kim, Y. Convolutional neural networks for sentence classification. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, 25–29 October 2014.
  15. Ranoliya, B.R.; Raghuwanshi, N.; Singh, S. Chatbot for university related FAQs. In Proceedings of the 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Manipal, India, 13–16 September 2017; pp. 1525–1530.
  16. Khan, Y.; Mehmood, Z.; Shahid, M.K.; Ibrahim, M. Analysis of Consumer Perception towards Telecommunication Services. J. AMH Int. 2012, 3, 89–98.
  17. Ibrahim, M.; Shahid, M.K.; Ahmed, S.F. The impact of telecom services characteristics on consumer for use in Pakistan. Adv. Econ. Bus. 2014, 2, 172–179.
  18. Sayed, A.F.; Shahid, M.K.; Ahmad, S. Impact of Mobile Payment Applications and Transfers on Business; IGI Global: Hershey, PE, USA, 2020; pp. 253–269.
  19. Baiti, Z.N.; Nugroho, F. Aplikasi Chatbot ‘MI3’ Untuk Informasi Jurusan Teknik Informatika Berbasis Sistem Pakar Menggunakan Metode Forward Chaining. Matics 2013, 5, 9.
  20. Argal, A.; Gupta, S.; Modi, A.; Pandey, P.; Shim, S.; Choo, C. Intelligent travel chatbot for predictive recommendation in echo platform. In Proceedings of the 2018 IEEE 8th Annual Computing and Communication Workshop and Conference, Las Vegas, NV, USA, 8–10 January 2018; pp. 176–183.
  21. Kjersti, B. The Chatbot Will See You Now. Can Chatbots be a Supplementary Element in Doctor-Patient Communication? Norwegian University of Science and Technology: Trondheim, Norway, 1999.
  22. Cui, L.; Huang, S.; Wei, F.; Tan, C.; Duan, C.; Zhou, M. Superagent: A customer service chatbot for e-commerce websites. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, ACL 2017, Vancouver, BC, Canada, 30 July–4 August 2017; System Demonstrations. pp. 97–102.
  23. Shah, H.; Warwick, K.; Vallverdú, J.; Wu, D. Can machines talk? Comparison of Eliza with modern dialogue systems. Comput. Hum. Behav. 2016, 58, 278–295.
  24. Gbengaa, L.O.; Oluwafuntob, O.T.; Oluwatobic, A.H. An Improved Rapid Response Model for University Admission Enquiry System Using Chatbot. Int. J. Comput. 2020, 38, 123–131.
  25. Christopoulos, A.; Pellas, N.; Laakso, M. A Learning Analytics Theoretical Framework for STEM Education Virtual Reality Applications. Educ. Sci. 2020, 10, 317.
  26. Goode, L. Get Ready to Hear a Lot More About’XR. Wired, 1 May 2019. Available online: https://www.wired.com/story/what-is-xr/(accessed on 29 August 2020).
  27. Gigante, M. Virtual reality: Definitions, history and applications. In Virtual Reality Systems; Earnshaw, R.A., Gigante, M.A., Jones, H., Eds.; Academic Press: San Diego, CA, USA, 1993.
  28. Christopoulos, A.; Conrad, M.; Shukla, M. Increasing student engagement through virtual interactions: How? Virtual Real. 2018, 22, 353–369.
  29. STEM Education Data. Available online: https://nsf.gov/nsb/sei/edTool/explore.html (accessed on 6 October 2020).
  30. Heim, M. Virtual Realism; Oxford University Press: Oxford, UK, 1998; pp. 162–167.
  31. Wolfartsberger, J. Analyzing the potential of Virtual Reality for engineering design review. Autom. Constr. 2019, 104, 27–37.
  32. Pellas, N.; Kazanidis, I.; Palaigeorgiou, G. A systematic literature review of mixed reality environments in K-12 education. Educ. Inf. Technol. 2019, 25, 2481–2520.
  33. Makransky, G.; Borre-Gude, S.; Mayer, R.E. Motivational and cognitive benefits of training in immersive virtual reality based on multiple assessments. J. Comput. Assist. Learn. 2019, 35, 691–707.
  34. Christopoulos, A.; Kajasilta, H.; Salakoski, T.; Laakso, M.-J. Limits and Virtues of Educational Technology in Elementary School Mathematics. J. Educ. Technol. Syst. 2020, 49, 59–81.
  35. Pellas, N.; Fotaris, P.; Kazanidis, I.; Wells, D. Augmenting the learning experience in primary and secondary school education: A systematic review of recent trends in augmented reality game-based learning. Virtual Real. 2018, 23, 329–346.
  36. Online High School In Japan Enters Virtual Reality. Available online: blogs.wsj.com (accessed on 7 April 2016).
  37. DSTS: First Immersive Virtual Training System Fielded. Available online: www.army.mil (accessed on 16 March 2017).
  38. Moro, C.; Štromberga, Z.; Stirling, A. Virtualisation devices for student learning: Comparison between desktop-based (Oculus Rift) and mobile-based (Gear VR) virtual reality in medical and health science education. Australas. J. Educ. Technol. 2017, 3.
  39. How NASA is Using Virtual and Augmented Reality to Train Astronauts. Available online: https://unimersiv.com/how-nasa-is-using-virtual-and-augmented-reality-to-train-astronauts-37/ (accessed on 15 March 2017).
  40. Virtual Reality in Mine Training. Available online: www.cdc.gov (accessed on 9 November 2018).
  41. Strickland, J. How Virtual Reality Military Applications Work. Available online: howstuffworks.com/virtual-military (accessed on 15 April 2021).
  42. Omer, M.; Hewitt, S.; Mosleh, M.H.; Margetts, L.; Parwaiz, M. Performance evaluation of bridges using virtual reality. In Proceedings of the 6th European Conference on Computational Mechanics 7th European Conference on Computational Fluid Dynamics (ECFD 7), Glasgow, UK, 11–15 June 2018.
  43. Learning Analytics & Knowledge: February 27–March 1 2011 in Banff, Alberta. Available online: https://tekri.athabascau.ca/analytics/ (accessed on 3 March 2021).
  44. Manyika, J.; Chui, M.; Brown, B.; Bughin, J.; Dobbs, R.; Roxburgh, C.; Byers, A.H. Big Data: The Next Frontier for Innovation, Competition, and Productivity; McKinsey Global Institute: New York, NY, USA, 2011.
  45. Kiron, D.; Shockley, R.; Kruschwitz, N.; Finch, G.; Haydock, M. Analytics: The Widening Gap. MIT Sloan Management Review, 7 November 2021. Available online: https://sloanreview.mit.edu/projects/analytics-the-widening-divide/(accessed on 28 March 2021).
  46. Siemens, G. Learning Analytics: The Emergence of a Discipline. Am. Behav. Sci. 2013, 57, 1380–1400.
  47. Gašević, D.; Dawson, S.; Siemens, G. Let’s not forget: Learning Analytics are about learning. TechTrends 2015, 59, 64–71.
  48. Cooper, A. What is analytics? Definitions and essential characteristics. JISC CETIS Anal. Ser. 2012, 1, 1–10. Available online: http://publications.cetis.ac.uk/wp-content/uploads/2012/11/What-is-Analytics-Vol1-No-5.pdf (accessed on 4 April 2021).
  49. Siemens, G. What Are Learning Analytics? Available online: elearnspace.org (accessed on 25 August 2010).
  50. Greller, W.; Drachsler, H. Translating Learning into Numbers: Toward a Generic Framework for Learning Analytics. Educ. Technol. Soc. 2012, 15, 42–57.
  51. Picciano, A.G. The Evolution of Big Data and Learning Analytics in American Higher Education. J. Asynchronous Learn. Netw. 2012, 16, 9–20.
  52. Powell, S.; MacNeill, S. Institutional readiness for analytics. Cetis Anal. Ser. 2012, 1, 1–11.
  53. Gašević, D.; Kovanović, V.; Joksimović, S. Piecing the Learning Analytics puzzle: A consolidated model of a field of research and practice. Learn. Res. Pract. 2017, 3, 63–78.
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