Using Artificial Intelligence To Improve University Education

Benefits of AI-based Education System for University

This project would involve use of Artificial Intelligence for improving education through the development of the pedagogical model for a University. The AI based education system would be developed and installed in the college and would be used by the students and the faculties associated with the university.

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AI technologies can be used for imparting education in students under different scenarios. A system can deliver the lectures designed by tutors to students directly through a transmission based AI system or can build a constructive studio for students to learn on their own with their own pace and style. Another scenario could be a negotiation between the two styles of learning such that both teachers led and activity based lessons are provided to a student. Each of these scenarios can present different ways of knowledge acquisition. In transmission, learning is achieved through drills, practice, lectures, and reading while a studio presents a more collaborative and project based learning to students (Andriessen & Sandberg, 1999)

Intelligent systems are being used for tutoring for a long time now and the paradigm has been dominating the field of Artificial Intelligence such that computer-based learning systems have been formed to be used by tutors in different ways. A large number of AI interests have already been experimented to operationalize the possibilities of rendering education with support from the understanding of the cognitive systems in the field of education. AI has not just helped students in the assimilation of knowledge but also helped in creating a collaborative environment for both students and teachers allowing them to interact and make use of project-based learning systems. A combination of technologies like information databases, network communication, and multimedia systems have helped in rendering educational services in new formats (ziaaddini & Tahmasb, 2014).

However, to be able to build a sound technology that can assist in student learning, it is also essential to understand the cognitive processes, student learning models and tutoring components as they could be used for the development of modules in the AI setting. This research would begin to explore these areas of education to determine the possibilities of developing an AI based tutoring solution which would benefit students of the university in the long run by making education more engaging and fun for them (BLANCHARD, VOLFSON, HONG, & LAJOIE, 2005).

The university management would be the sponsor for this research and thus, would provide the needed funds for its execution that includes research, development of software modules, testing, and installation of the system in the campus. With the new AI system installed for assisting student learning, the college is expected to receive following benefits:

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  • Students would be able to receive learning even off site which would allow college to extend their services to more students who may not be attending lectures in the college
  • The new formats such as multimedia systems would make the learning more engaging for students and thus, they would have their interests increased in learning
  • Collaborative AI systems would allow students to work in collaboration with each other and execute projects for practical lessons
  • The new system would also provide support to the tutors who could deliver lectures and other materials to students through the system as well as build plans for their support (Becker, 2017)

Pedagogical Models for a University

The aim of the current research is to understand how pedagogical systems work in a university and explore the possibilities of development of an AI based tutoring system for the university to enhance its education delivery system.

The study would be answering following research questions:

  • What are the components of a tutoring system used in the university?
  • What is the student learning model used in the university?
  • What kind of AI technologies are available to be used in the education system?
  • How would AI based learning system benefit the university and its students?
  • How can the AI based student learning system can be developed?(Beck, Stern, & Haugsjaa, 2005)

A research is a scientific method used for inquiring subject in depth so that some major problems related to the domain can be solved or major questions that can add to the knowledgebase can be answered. A research process involves identification of problems, exploration of the literature on the subject, development of arguments, development of hypothesis, collection of data, testing of hypothesis, analysis of data, and drawing conclusions from them.

The current research which is proposed is a system based research which makes use of both the basic methods of inquiry and the applied research processes. A problem solving approach would be utilized in this project for the development of the AI based education system prototype for which a formative research would be utilized so that major education needs can be identified and data would be collected to address the related issues  (Numanaker, Chen, & Purdin, 1991).

This project would make use of soft systems development methodology which would involve research on the information system of the university and the development of the new cognitive AI based education module for tutoring. It would involve collection of data on the needs of the university in terms of tutoring through AI. The project can be used a number of available project implementation methodologies such as the following:

  • Waterfall model would provide a linear sequence in which the project can be developed beginning with data gathering to designing, module development, testing, and installation
  • Prototyping can be used for partial development of the first phase of the project so that it can be tested by users and modifications can be made if required.
  • Spiral methodology or agile systems provide an opportunity for incremental development of the system which can be useful for the university in case there would be changes required during development beyond the initial design (CMS, 2008)
  • Rapid application development is an iterative method of implementation that can help university develop and implement solution in less time and thus, incur less development cost (Burstein & Gregor, 1999)
  • Rapid Application Development is another development method that uses iterative pattern for the implementation of a high quality networking solution in less time and at low cost 

As the system would be majorly developed by students, there are chances of several changes to be made in the mid and thus, waterfall methodology would not be suitable. A spiral methodology can be too time consuming while prototyping could be very expensive. A rapid application development needs a high level of expertise which students may not have. Thus, an incremental agile development methodology would be used for the current project development. 

The data would be collected majorly from two types of sources including the literature done on the subject available from secondary sources like journals, books, and websites and the primary source of information which would be obtained from the university artefacts that would be explaining the pedagogical models, student learning model, and tutoring components to the researcher.  The secondary data would be used for the development of the modules with support from the functionalities decided by the study on the cognitive systems to be used in the university. For the collection of the primary data on the university system and its future requirements for the new AI based system, students and tutors from the college would be interviewed.

Research Process

There are compliance requirements for the use of AI in education such as:

  • The systems developed must respect the privacy of the students using the system and thus, have appropriate measures taken during development
  • Appropriate security measures must be taken so as to protect the personal data of the students and the tutors participating in the system
  • The development should be purely focused on the welfare of the students and should not introduce any educational practice just for the sake of revenue generation.

The data that would be obtained both from the secondary and primary sources would be analyzed using a content analysis and the results would be obtained in the form of the relationships and patterns identified. This would help in the identification  of the themes related to the learning model and tutoring components that would be used for planning the development of the modules for tutoring for the university.

The project would involve requirement gathering for understanding of the project requirement, acquisition  of the AI technologies needed for the development, coding of the systems, development of the tutoring components as modules, testing of the developed system, and installation of the same in the university for the delivery.

  • Learning model: This would have the information of the students who are categorized based on their learning paths and capabilities
  • Tutoring Components: These would include the student model, knowledge model, communication model, a pedagogical module, and an expert module
  • AI Modules: Based on the learning model and the identified tutoring components, different cognitive learning modules would be developed using AI capabilities.
  • Research Project initiation
    • Developing scope
    • Resource identification
    • Roles and responsibilities
  • Data Gathering
    • University facts
      • Tutoring components
      • Student Model
    • Research documents
    • AI Applications
    • University requirements
  • Requirement Identification
    • Modules
    • User requirements
    • Functional requirements
    • Non-Functional requirements
  • Requirement Verification & Validation
    • User requirement verification
    • User requirement Validation
  • Development
    • Module development
    • Module testing
    • Integration
    • Installation

Risks are a part of any project work and so would be the case with the current project which is likely to face certain rusks. Identification and analysis of the potential risks before beginning the project can help a researcher or developer minimize them while remain prepared to deal with those still occurring during the project. The plan should thus be developed considering the possibilities of these risks so that they are avoided or appropriately managed without affecting the project deliverables. The risks that can occur on the current project include:

  • Lack of resources: The researcher would need some resources to gather information needed for the development of this project as well as for the actual development of the system. There could be a case when the available research are insufficient in which case, the risk can cause delays and limitations. An appropriate prediction of the possibilities and development of the contingency measures can be helpful (Cooper, 2004) 
  • Lack of understanding requirements: There can be a possibility that the development team does not understand the requirements of the project quite well which can result into  demands for rework by project stakeholders at later stages of the project. Use of an incremental methodology can avoid this to a certain extent but the involvement of stakeholder during requirements gathering would further help in reducing the possibilists of facing such situations (Gotterbarn & Rogerson, 2005)

WBS

Task Name

Duration

Start

Finish

Predecessors

0

Research project – Artificial Intelligence

113 days

Wed 01/08/18

Fri 04/01/19

1

   Research Project initiation

5 days

Wed 01/08/18

Tue 07/08/18

1.1

      Developing scope

3 days

Wed 01/08/18

Fri 03/08/18

1.2

      Resource identification

3 days

Wed 01/08/18

Fri 03/08/18

1.3

      Roles and responsibilities

5 days

Wed 01/08/18

Tue 07/08/18

2

   Data Gathering

38 days

Wed 08/08/18

Fri 28/09/18

2.1

      University facts

8 days

Wed 08/08/18

Fri 17/08/18

2.1.1

         Tutoring components

8 days

Wed 08/08/18

Fri 17/08/18

2,3,4

2.1.2

         Student Model

8 days

Wed 08/08/18

Fri 17/08/18

2,3,4

2.2

      Research documents

8 days

Mon 20/08/18

Wed 29/08/18

7,8

2.3

      AI Applications

10 days

Thu 30/08/18

Wed 12/09/18

9

2.4

      University requirements

12 days

Thu 13/09/18

Fri 28/09/18

10

3

   Requirement Identification

32 days

Mon 01/10/18

Tue 13/11/18

3.1

      Modules

5 days

Mon 01/10/18

Fri 05/10/18

11

3.2

      User requirements

9 days

Mon 08/10/18

Thu 18/10/18

13

3.3

      Functional requirements

9 days

Fri 19/10/18

Wed 31/10/18

14

3.4

      Non-Functional requirements

9 days

Thu 01/11/18

Tue 13/11/18

15

4

   Requirement Verification & Validation

5 days

Wed 14/11/18

Tue 20/11/18

4.1

      User requirement verification

5 days

Wed 14/11/18

Tue 20/11/18

16

4.2

      User requirement Validation

5 days

Wed 14/11/18

Tue 20/11/18

16

5

   Development

33 days

Wed 21/11/18

Fri 04/01/19

5.1

      Module development

10 days

Wed 21/11/18

Tue 04/12/18

18,19

5.2

      Module testing

8 days

Wed 05/12/18

Fri 14/12/18

21

5.3

      Integration

5 days

Mon 17/12/18

Fri 21/12/18

22

5.4

      Installation

10 days

Mon 24/12/18

Fri 04/01/19

23

References

Andriessen, J., & Sandberg, J. (1999). Where is Education Heading and How About AI? International Journal of Artificial Intelligence in Education, 130-150.

Beck, J., Stern, M., & Haugsjaa, E. (2005). Applications of AI in Education . Cite Seerx.

Becker, B. A. (2017). Artificial Intelligence in Education: What is it, where is it now, where is it going? Brett Becker.

BLANCHARD, E. G., VOLFSON, B., HONG, Y.-J., & LAJOIE, S. P. (2005). Affective Artificial Intelligence in Education: From Detection to Adaptation . ATLAS Laborator.

Burstein, F., & Gregor, S. (1999). The Systems Development or Engineering Approach to Research in Information Systems: An Action Research Perspective. Australia: Monash University .

CMS. (2008). SELECTING A DEVELOPMENT APPROACH . CMS.

Cooper, R. (2004). Risk Analysis and Preventing Information Systems Project Failures. School of Computing and Mathematical Sciences.

Gotterbarn, D., & Rogerson, S. (2005). RESPONSIBLE RISK ANALYSIS FOR SOFTWARE DEVELOPMENT: CREATING THE SOFTWARE DEVELOPMENT IMPACT STATEMENT. Communications of the Association for Information Systems, 730-750.

Numanaker, J. F., Chen, M., & Purdin, T. D. (1991). Systems Development in Information Systems Research. Journal of Management Information Systems, 7(3), 89-106.

ziaaddini, M., & Tahmasb, A. (2014). ARTIFICIAL INTELLIGENCE HANDLING THROUGH TEACHING AND LEARNING PROCESS AND IT’S EFFECT ON SCIENCE-BASED ECONOMY. International Journal on Soft Computing, Artificial Intelligence and Applications (IJSCAI), 1-7.