The Role of Artificial Intelligence in Adult Education in the UK

1. Introduction

A study from the University of Tennessee stated that cognitive tutors are associated with substantial learning gains compared to traditional classroom instruction and other forms of educational software.

AI tutors are built to promote improvements in students' knowledge, academic behaviours, and attitudes by providing individualised support. B. Woolf suggested that intelligent teaching systems have the potential to improve education for a wide diversity of educational goals in a cost-effective way. This is said to be achieved by simulating human tutors, aimed at helping students learn the material that is taught in school, but in practising the delivery of instruction and providing immediate feedback that adapts to the student.

AI was founded as an academic discipline in 1956. The field has experienced several hype cycles, followed by disappointment and general scepticism about AI's value, which has ultimately led to funding cuts in the late 1990s. Today, AI has evolved and is being used in education to enhance students' learning. With current education technology starting to peak, the existence of AI tutors to aid students in learning is starting to become a noticeable reality.

The use of artificial intelligence (AI) technology is becoming more prevalent in our everyday lives, and favourably so. Beginning in 1956, AI was a term that was used to describe machines with the ability to simulate human intelligence. The general idea at the time was to create a machine that could mimic a human being and be capable of learning and solving problems. These early years in AI research are some of the most controversial in the history of computer science. The funding of projects for the purpose of mimicking the human mind was unrealistic and made AI researchers the target of criticism.

2. Benefits of Artificial Intelligence in Adult Education

AI could also provide a more personalized learning experience using virtual intelligent agents. These intelligent characters could enhance learning by offering multimedia content and providing learning support to the student. The character could help to create an engaging learning environment through a game-based learning system. This type of environment would be of high interest to students and would keep them motivated as the intelligent agent continually adapts to the student's individual learning style. In doing this, the characters would also develop learning communities, based around a shared interest in the characters themselves. Students of all ages often have access to the internet at some location or the other and through the use of web technology, these characters could be accessed at any time or place. This, in turn, could lead to increased knowledge retention, as there are no time restrictions on when the knowledge needs to be learned. Finally, virtual intelligent agents could provide significant learning support to people with learning disabilities. The character would offer a patient and understanding learning companion which would suit the needs of a person with a learning disability, and AI can adapt learning resources to cater for an individual with special needs.

No matter the source of the content, AI could make getting an education a more efficient and personalized experience in the future. AI systems could potentially track a student's progress by assessing the user's knowledge, the effectiveness of the instruction, and the learner's knowledge retention. AI could be used to understand the behavior of learners and to create adaptive learning paths that are specific to the student. Moreover, the data that is collected from the interaction could be represented in a visual and meaningful way for the educator, which would help them understand how the student is learning and the areas in which the student is struggling. This would help educators make quick and effective decisions on what changes are needed in instruction to get the student back on track. Data could also be used to automate the process of creating an effective learning strategy, making it the defining feature in the next generation of educational technology.

2.1. Personalized Learning Experience

The utilization of AI presents the potential for a more adaptive learning pathway for students. AI can track the progress of an individual user and bring forth relevant materials to guide the user through a curriculum. In many ways, the application of AI in education can serve as a virtual teaching assistant that caters to each student's needs. An example of AI that is already being used in education is NetTutor's Active Lab. It reads the students' responses to various activities in order to pinpoint any misconceptions that they may have. NetTutor then responds by offering personalized assistance to the student, seeking to improve the student's critical thinking process. AI can assist adult learners in that it can guide them through a curriculum that is relevant to their career or personal goals. Adult learners are self-motivated and have a specific reason for seeking education. AI can help them progress more quickly towards their end goal. For example, a busy medical student or intern trying to learn medical Spanish can utilize AI to bring forth Spanish vocabulary and phrases that are tailored specifically for a medical context. This student would not have to waste time sifting through general-purpose language learning tools and could therefore focus more on learning medical terminology.

2.2. Enhanced Accessibility and Flexibility

AI also has the potential to enhance the accessibility and flexibility of learning for non-traditional students through tailored educational resources and support systems. An intelligent user interface (IUI) refers to any software interface that uses AI techniques to adapt to the changing needs of the user. IUIs have been used in educational software for children with great success, and there is potential for developing IUIs for adult learners that could help to simplify the interaction with online course materials. A great strength of an IUI is in its ability to automate routine tasks, thereby freeing cognitive resources of the user for other tasks. This is especially important for adult learners studying part-time who must balance learning with work and family commitments. By automating tasks and simplifying the learning process, IUIs can help to reduce the cognitive load associated with online learning, increasing the effectiveness and efficiency of learning for these students.

As non-traditional students make up an increasing portion of the college-going population, there is a need to better understand their specific educational needs and how institutions can use technology to address these needs. Artificial intelligence is particularly relevant to the needs of adult learners who often require highly flexible learning opportunities to fit with their work and family responsibilities. In numerous interviews with adult learners, the flexibility of when and where the learning occurs was a highly desired feature of online education. AI, in the form of intelligent tutoring systems, provides a mechanism to automate instruction, allowing learning to occur asynchronously at a time and location that is convenient for the student. ITSs have been shown to be nearly as effective as human tutors in a broad range of topic areas, providing immediate and delayed learning gains, and have proven to be more effective than classroom instruction in some. With the vast amount of data available about the knowledge and learning preferences of individual students, ITSs can adapt instruction to the specific needs of a student, providing a highly personalized learning experience. This is a key feature of AI with respect to education and has direct implications to learning effectiveness and student satisfaction for non-traditional students.

2.3. Real-time Feedback and Assessment

AI-based assessment offers the prospect of an endless supply of assessment and feedback for learners. This is because AI-based assessment can be almost instantaneously auto-generated and administered and can be deep in its feedback. AI uses learning analytics that enables learners and educators to receive feedback that can be used in improving the learning experience. For example, AI could provide feedback on a given piece of written work that highlights a pattern of errors, say in grammar or structure, and then follow that up with a targeted learning resource to address the problem area. AI assessment also has the advantage of relieving the assessment burden on staff involving tasks such as grading and can dramatically reduce the turn-around time in assessment as compared to traditional methods.

Real-time feedback and assessment are one of the most important elements in effective learning because it provides the learner with information on his or her performance. AI enables the creation of adequate assessment techniques through the use of various test item analysis methods. What this means is that AI can be used to determine how effective a test question is by using statistical analysis on vast amounts of student performance data. If a question is not providing a reasonable level of discrimination between high and low-achieving students, or if many students are getting it wrong, the question is not a good one. AI analysis can keep tweaking the question in an iterative manner until it is just right. AI also enables knowledge-based systems that can provide immediate and specific feedback to users, the most familiar example being the adaptive feedback given by training and My-AI systems. These systems are highly relevant to the needs of each individual user and provide feedback that is immediate and correct.

3. Challenges and Concerns

There is potential for stand-alone intelligent agents to disadvantage students from low-income families and minority groups. If these technologies are cost-effective and, in some cases, cheaper than providing human teaching, there is a risk that schools will substitute human teaching posts for intelligent agents. Though the agents may be as effective as human teachers, there are social benefits to employing teachers in economically deprived areas. Often, teaching is seen as a respected route of employment for intelligent individuals within such communities, and the loss of teaching posts may be damaging to the morale of the community in question. Furthermore, children benefit from the presence of successful role models and positive influences from the community, a factor which may be absent from an intelligent agent with no links to the community in which the school is situated. This could lead to relative deprivation for children from such areas.

3.1. Ethical Implications of AI in Education

In contrast to the potential benefits that have been put forward, some concerns have been raised regarding the implementation of AI in education. Without an appropriate understanding of the implications of introducing intelligent agents into education, it is all too easy to assume that the automation of educational processes is a panacea for deep-seated problems. However, this is not yet the case and there are good reasons to be cautious. There are several challenges and concerns facing the implementation of AI in education, which will be discussed in the following sections. These include ethical concerns and issues relating to the effects on employment in the education sector, as well as more generalized concerns about the security and privacy of data.

3.1. Ethical Implications of AI in Education

There has been much dialogue recently on the issue of AI consciousness and ethics. Even if technology develops to the point where an AI tutor knows the right answer to every learning difficulty a student might encounter, is AI better than a human tutor, or even a good set of learning materials? If the machine knows the answer, but can only explain it in a way that is too difficult for the student to understand, or if repeating the same question in a different form causes the AI to give a different correct answer, is it a satisfactory learning experience? Will the student feel dumb or become frustrated if he is continuously outsmarted by the machine on a wide variety of intellectual tasks? Teachers and parents often encourage students by telling them that persistence in the face of failure is a crucial part of learning, but failure is precisely the issue that an AI tutor might not allow. A student may intentionally or unintentionally train the AI to feed him the right answer in the quickest ways possible. Failure in this case to utilize AI tutoring.

AI systems offer increased possibilities for adaptation to the individual student's skill level of learning, but at present, consumer AI has vastly outstripped state-of-the-art AI for education. A natural concern is that this could spur further segregation and inequality in education. Those who can afford and in some cases demand the best for their children might provide tailored AI and a highly efficient learning environment. In contrast, poorly funded schools might become the testing grounds for new technologies with unproven effectiveness, while these technologies are relatively denied to students of wealthier or more demanding parents. This might in turn lead to a growing technological achievement gap in students, further marginalizing the less well-off. This concern still has time to be addressed, since there is currently very limited proven effectiveness for AI in education, and it is not too late to ensure that future AI development is guided by evidence from sound educational practice and learning sciences.

3.2. Potential Job Displacement

An argument states that those who are able to afford it will resort to private education to ensure a safe job for their children's future. While not everyone can afford private schooling, those who can will want to ensure that the money spent on educating their children will lead to a successful career. This could potentially lead to a two-tiered society in which the upper class are the overseers and managers of AI in a more narrowly skilled workforce, while the AI performs lower-level activities carried out by today's workers. Adults who are displaced into unemployment or early retirement before the AI education era reaches its steady state will face difficulties finding alternative employment with similar pay and job satisfaction given the changes in skill demands of the workforce. This will create a large social and economic divide between the previous education workforce and their dependents who receive the benefits of education provided by AI's.

As AI systems and platforms start to replace the jobs of teachers and other education workers, there will be a total loss of crafting professions. This will diminish the amount of human intelligence in the field of education, which is a risky situation. Today's jobs are increasingly about cognitive and non-routine tasks, and experts claim that many jobs that exist today will not exist in the future. Educational professionals will need to consider how to both harness and instil human intelligence and creativity throughout the AI. In more technical terms, AI will automate lower-level activities, requiring a shift in the skills and knowledge of the human workforce towards higher-level cognitive and non-routine tasks. An effective response will require changes to curriculum content and teaching methods at all levels of education. However, it is unclear whether this will create new jobs or a net loss of jobs in the CI to a more narrowly skilled workforce.

3.3. Data Privacy and Security

Data privacy has turned into a noteworthy worry because of the utilization of instructive information to settle on choices about which courses or learning systems to offer a student. At a sensible level, taking data from an appraisal of a student's learning style, inclinations, or execution in a past instructive program, to put the student in the most appropriate course or program, is something worth being thankful for: it speaks to an enhancement over the present circumstance where numerous such choices are made utilizing minimal more than instinct. In any case, the utilization of outsider organizations to settle on these choices may prompt mission float, as the aim of these organizations might be to institutionalize and oversimplify the choice procedure with an end goal of spare expenses across numerous instructive organizations. This could block certain long shots or hazard-taking people from getting to imaginative or experimental projects that have no reputation for their sake. More regrettable, oversimplified position techniques could order students with a few qualities as more propelled programs than is proper, setting them up for disappointment. This speaks to a genuine moral danger, and it won't generally be evident whether a given instructive foundation has allowed such an arrangement through some concurred administration-level contract with an organization selling AI items.

4. AI Tools and Technologies in Adult Education

Intelligent Tutoring Systems (ITS) has been a key area of AI research and development. ITS is an example of an expert system, a computer-based teaching system that attempts to simulate a human tutor. They are "intelligent" because they can adapt to the learning needs of the students. They typically do this by assessing the students' knowledge of a subject and then providing tailored instruction and feedback. ITS are particularly useful in adult education where there are a wide range of learner needs. They enable adults to learn at their own pace and provide a source of learning that is tailored to the individual. This is important for adults as they can often be discouraged when faced with a difficult learning task. If the ITS is able to provide the right type of encouragement and help, it can lead to increased motivation and self-esteem. ITS requires a large knowledge base on the subject to be taught and so are not widely available for all subject areas. However, if they become more accessible, ITS could be an important tool for adult education in the future.

AI has impacted adult learning and education in many ways. AI tools used in adult education can bring fundamental changes in how adults apply their skills and learning challenges and experiences. AI tools and technology can offer new opportunities for adult learning through online and distance learning. They can also provide tools that support the self-directed learning process and make learning an integral part of everyday life.

4.1. Intelligent Tutoring Systems

The ability to comprehend and engage with one's learning is not consistent throughout every individual. Intelligent Tutoring Systems (ITS) are a technology that can profoundly tailor to the learner's needs. ITS provides a learning environment in which the learner interacts with a computer program that guides them through various exercises and feedback, customizing the learning experience to the needs of the student. It will provide high-quality, consistent and relevant instruction to all students, reducing the performance gap often seen between skilled and less skilled students when learning from human teachers. ITS can fully automate the instruction or can provide adjuncts to a teacher in a classroom. It is a real benefit to adult learners as it gives them the autonomy to learn in their own time and tailor the instruction to skills and knowledge they might have obtained during previous work. This is important for adult learners whose skills are outdated and in need of learning technology to update their current knowledge. ITS can be especially beneficial for disabled students. In a study done by Wintersgill et al, ITS was used to help teach basic computer literacy in word processing to young adults with cognitive disabilities. This has been highly successful as the program can provide more patient and consistent instruction than a human teacher who might get frustrated or move on to more advanced instruction. ITS technology is constantly being developed and has the potential to serve many purposes in the future of adult and higher education. The Department of Defense has been sponsoring the latest development projects for ITS and has found that it could reduce the need for classroom-based training and raise the effectiveness of training. As ITS technology becomes more advanced it could also apply to teaching methods for distance learning.

4.2. Virtual Reality and Simulations

The final form of AI and advanced technologies seen to be potentially beneficial for adult learning is in the form of VR and simulations. This is evident by the rate of technological advancements in this area and heavy investment by various firms with relation to other AI software. As a wrap-up of the previous EATEL workshop series, a final workshop focused directly on the potential future applications of AI for teaching and learning. The 2-day workshop "Artificial Intelligence in Technology Enhanced Learning" was co-located with the Intelligent Tutoring Systems International Conference and provided multiple insights into AI developments for the future. On the first day of the workshop heard six different talks on topics that may influence the future of Ed-Tech. An additional H2020 project called ImREAL has been identified which integrates Intelligent Tutoring Systems and Virtual Reality to create emotionally intelligent pedagogical agents. This may optimize learning experiences for students and potentially increase completion rates for online courses. This links to the ADORE project in the way that it seeks to dynamically respond to a learner's affective state, but as previously mentioned adds an extra dimension to agent interactivity.

4.3. Adaptive Learning Platforms

Heuristic methods were developed over ten years ago, and an example of a modern system that uses these methods is the WHY system. This system is based on the ACT cognitive architecture and uses a bug model to teach programming in LISP. An evaluation of this system compared with a non-intelligent teaching system demonstrated significant gains for the intelligent system. Although this was just a simulation, a more recent controlled study with an intelligent tutoring system known as SQL tutor showed significant learning and retention gains and more efficiency in learning. Other intelligent tutoring system research has led to the development of authoring tools for these systems, for example, the ASPIRE tool which has been used to build medical instruction systems.

An adaptive learning system is a type of educational system that uses computers as interactive teaching devices. Computers adapt the presentation of educational material according to students' learning needs. The literature on this type of system is not clear, but it is important to clarify the boundaries between intelligent tutoring systems and adaptive systems. These two areas have a significant overlap, and there is some confusion between the two. The first section therefore introduces recent work in intelligent tutoring systems, which has been focused on the application of cognitive science theories to computer-based instruction.

5. Implementation Strategies for AI in Adult Education

One strong strategy for AIED implementation in adult learning requires using established cognitive models and intelligent tools of instruction to better identify and teach that knowledge and cognitive skills which are prerequisites to learning domain content, as well as provide instruction on domain content. Early work by Frasson and Gauthier provides a good example of this approach. They employed an intelligent tutoring system designed to provide diagnosis and remediation of students' data structure knowledge. Following a similar line, one might also consider the potential of using AIED to help students acquire cognitive and metacognitive learning strategies that are essential for success in adult distance education (e.g. time management, and elaboration strategies). This research theme corresponds to the distinction made in the Opening Remarks between the potential of AIED to facilitate adult learning and AIED to facilitate education about learning.

5.1. Training and Professional Development for Educators

Thus, there is a need for continued professional development, training, and training materials for teachers and adult educators to create an understanding and disseminate best practices of how AI can be used to benefit learners in the adult education sector. The UK has a mixed economy for teaching and training in adult education, involving further and higher education institutions, adult learning centres, and a range of private and voluntary sector organizations. Therefore, professional development needs to be targeted at educators working in different settings.

For example, the AI techniques used in WBLabs are quite complex. Work on tailored education involves the development of intelligent tutoring systems which model students' learning, enabling dynamic generation of material and tailoring tasks to the individual student's strengths and weaknesses. Cognitive Tutors are an example of this kind of system. Learners using these systems will be able to work at their own pace, receiving hints and feedback. Such systems are a world away from regular tools and resources used for curriculum development. For educators to utilize these new technologies effectively, in some cases, it may require the development of specific training materials such as the completion of an online course to give teachers the skills required to implement intelligent tutoring systems. In other cases, it may require further education of teachers, i.e., taking a module in cognitive psychology to allow them to make the best use of technology such as Cognitive Tutors when tutoring students.

In the previous sections, the essay has demonstrated a range of ways in which AI can impact adult learning. From improving learning platforms and providing tailored education in basic skills to increasing access for socially excluded groups to higher education, there are various potential benefits. However, if AI is to realize its potential, teachers, trainers, and adult educators need to be able to understand how to make the best use of these new technologies, and in some cases, to learn new skills.

5.2. Integrating AI into Existing Curriculum

If designed and deployed correctly, such learning resources can aid in the automation of formative assessment, giving students rapid feedback on their work without instructor intervention. Elegant solutions to this have been demonstrated in research and ITS systems like WayangOutpost, where Chemistry students can submit assignments and get instant feedback through working out automatically generated problems, and iList, which helps students learn Prolog by identifying common mistakes made with lists and generating custom questions to steer students in the right direction. It is likely, however, that the most effective and profound changes that AI can bring to bear on education will be through the development of intelligent learning environments.

Institutes can test, refine, and iteratively build AI into yearly student programs or courses aimed at increasing students' confidence and ability in the subject. This may involve integrating an AI tutor into a course where students are unable to tell if the tutor is a human or machine, working on real problems or specific projects in the discipline. If we consider the example of an intelligent tutor for mathematics, it might help a learner to have basic skill or understanding gaps identified through a diagnostic test and then propose a learning plan (including targeted mini-lessons and activities) capable of bringing the student up to a desired performance level.

5.3. Collaboration between Institutions and AI Developers

Boosting signals for AI developers from other industries to build education-specific AI tools and offering small-scale R&D funding can be the right policy to make this happen. AI has the potential to increase productivity and cost-effectiveness in the development of education tools and software. Therefore, guiding current developers of educational software to learn from experiences in other sectors may be more effective than persuading them to enter AI development firsthand. This may involve provisions of information as well as incentives for developers. For cases where it is more cost-effective to develop general AI tools first, it may be necessary for the public sector to get directly involved in the development of education-specific tools. It is also important to continue improving technology literacy amongst educators and educational researchers. AI has the potential to improve educational practices, but it may not be effectively utilized if there is limited understanding of the capabilities and limitations of AI among its intended users.

6. Case Studies: Successful AI Integration in Adult Education

The case at University X involves the sustained development of an AI tool called "FETLAR" (Feedback, Evaluation, Tutoring, and Learning using AI Resources), designed to make the first-year student experience more successful and rewarding. FETLAR is a joint project between computer scientists, the institutional teaching and learning unit, and a Statistics lecturer who is a domain expert in the tool's development. This collaboration is itself a positive educational process for the teaching and research staff involved and has helped to make the AI tool well-aligned with the pedagogical and pastoral needs of students and the staff that support them. FETLAR continues an existing line of work at University X on formative assessment and personalized learning. Data mining techniques have been used to map student pathways through module options, identifying both positive and negative drivers for various academic outcomes. FETLAR will use these maps and real-time data on student characteristics and behaviour to facilitate informed decision-making by students, constructive dialogue between students and personal tutors, and timely identification of students at risk of poor academic performance or dropout. The tool that has been developed is highly innovative, and while it is too early to present impact data, it is expected to have positive effects on a range of student outcomes and thus provides an exemplar of AI usage for personalized learning in Higher Education.

This section examines two such instances where AI has delivered very different instruments for transforming adult learning. In both cases, this has involved exploiting the power of data analysis for more personalized learning, but the tools used and contexts are highly varied. The case studies are of AI integration from "University X," a campus-based red-brick university, and "Organization Y," a national NGO delivering adult learning via further education colleges.

Educational settings must continually adapt and change to meet student needs as they seek to develop their potential. Lifelong learners now have an extensive array of digital and distance learning opportunities available to them. Artificial Intelligence (AI) can be employed to personalize and enhance the learning experience, and it is increasingly being introduced in various forms across the adult learning sector in the UK.

6.1. University X: Transforming Learning with AI

Measures of success have so far been positive, with a trial run of an adaptive presentation of learning material to students and its effect on learning showing a relative increase in learning of 30%. This was a very basic form of technology, and it is expected that a greater increase can be achieved with the completion and more advanced forms of the tools.

The first is accomplished with the Personal Profiling and Portal Systems, which provide a framework for adaptive systems based on a student's goals, preferences, and prior knowledge, which transfer into individual learning plans. The second is accomplished with OntoLearn, a system designed to assist educators in creating better learning material and making decisions about what to recommend and present to which students.

The DST is a joint project between the Knowledge Media Institute and the Centre for Research in Computing. The aim of this project is to create a multi-layered suite of software tools that can help make sense of the learning material and requirements of a learner to provide the best learning experience. This tool can be categorized into two goals: enhancing the student's ability to learn and improving the delivery of learning material.

The Open University (OU) is the largest academic institution in the UK and a world leader in flexible distance learning. To continue its mission of providing high-quality education for all, the OU is now focusing on a massive transformation of its teaching and learning methods. University X has started developing their own educational-based AI systems. Their biggest and most well-known project is known as the Development of Semantic Technologies (DST).

6.2. Organization Y: Empowering Adult Learners through AI

In addition, with an eye on improving student retention and graduation rates, the university has launched a prototype to enable an early alert system. This is an augmentation that provides an "at-risk indicator" to the learning teams and academic advisors about students who are deviating from a path of success, essentially triggering an intervention to get the student back on course.

The success of the Intelligent Agent prompted further enhancements. In 2010, an X Team was granted the opportunity to research natural language processing, which focuses on enabling the agent to effectively communicate with a student using language as if it were a human tutor/advisee dialogue. This enhances the conversation the agent has with the student and provides richer and more effective guidance.

The Intelligent Agent is a virtual adviser who provides individual students with real-time advice, support, and course-related information most relevant to their academic needs. This innovative feature enables students to engage in a dialogue with the agent to receive personalized learning opportunities, guidance, and support and be directed to resources and insights most relevant to their academic needs.

The University of Phoenix, an accredited institution, has been helping adult learners achieve their educational and career goals for 30 years. The university's continuing commitment to adult learners is to provide high-quality education where learning is brought to the student with innovative delivery methods. One such method is the utilization of the Intelligent Agent, a component of the Learning Genome Project, the university's groundbreaking educational platform that brings adaptive learning and cognitive learning theory together to create a more personalized and effective learning experience.

7. Future Trends and Possibilities

AI-Powered Personalized Career Guidance The traditional live lecture and tutorial can be located at a fixed time and place. In today's digital world, AI can automate a personalized learning pathway for individuals which is not restricted to semester time. AI-powered personalized career guidance is quite like the technology that powers product recommendation on websites. By utilizing data obtained from the learner, AI will be able to recommend the best possible career pathways with the required learning and skill development strategies to get there. In many cases, AI can generate hypothetical future scenarios and predict the possible outcomes of each decision. The Derbyshire Connexions Direct website is an early example of a decision tree of this nature.

The future of AI in adult education may be likened to the idea of a guided missile. A supersonic missile travels at such speed that it can automatically re-correct its navigation and still reach its pinpointed destination. Artificial Intelligence provides technological teaching and learning applications that will allow learners to access vast amounts of information anyplace, anytime, and at any pace. It will provide guided learning to an aspired academic or career pathway, with helpful and immediate corrective feedback. The future trend for AI in adult education is focusing on creating adept and proficient learners, thus increasing the likelihood of them reaching their targeted goals.

7.1. AI-Powered Personalized Career Guidance

While the specific technology described in the scenarios above does not yet exist today, these are natural evolutions of the current data mining and machine learning technologies. If properly leveraged, AI could be of great value in helping adult learners make sense of an increasingly complex and rapidly changing job market.

Ideally, intelligent agents would assist students throughout their learning experience, helping students learn which skills are most important for their desired career and suggesting what the most efficient path might be towards acquiring these skills. At every decision point along this path (e.g. whether to attain a 4-year or 2-year degree), agents would be able to weigh the pros and cons of different options in terms of how they will affect the likelihood of the student reaching their career goals. In a more advanced future scenario, intelligent agents could even help place students into jobs and help workers decide whether certain job offers are in line with their long-term career goals.

The concept of AI-assisted personalized career guidance in adult education is built on the premise that students deserve individualized support as they make important decisions about their future career paths. The beginning of such a system might only consist of a more advanced form of what exists today, with AI systems providing career aptitude tests and offering career suggestions based on student interests and abilities. The real value of AI in this context could come from the ability to continuously aggregate data from the labour market and use this data to make increasingly refined predictions about which career paths will lead to desired job outcomes for individual students. Over time, machine learning techniques could be used to further refine these predictions based on a continuously growing set of data about how different career paths have led to different outcomes for similar students.

7.2. Augmented Reality in Adult Education

This is the use of computer-assisted instruction and virtual reality that provides a new environment for students to learn by allowing them to use intelligent tutors from wherever they are and whenever they need. The students will not feel isolated since they know that help is always available. The intelligent tutor can also identify the students' strengths and weaknesses and provide immediate feedback with explanations. This is like the earlier mentioned personalized career guidance where it helps the students to take a more efficient path to their learning goals. In the long run, the use of AIED can be more cost-effective and efficient in the learning process. An example of this is the use of SHERLOCK, an intelligent diagnostic system designed to provide emergency medicine students with a self-directed learning tool to enhance learning of clinical reasoning. This tool uses an adaptive simulated environment for learning clinical problem-solving and has shown good results. This will coincide with the government's planned 25% cut in teaching funding for higher education by the year 2012 where, for the first time, students with disabilities will also receive funding for higher education directly from the government. AI and intelligent tutoring methods will allow these students to have an equal learning opportunity.

7.3. AI-Enabled Social Learning Networks

Learner support is a critical factor in the success of online learning, particularly for adult learners who often suffer from high attrition rates. Peer tutoring, mentoring, advising, and cognitive apprenticeships are effective forms of learner support. AI can play a role in providing these forms of support to learners in a cost-effective manner. For example, tutoring systems in the form of intelligent agents can provide immediate feedback and explanations to learners, learning companions can help facilitate the development of problem-solving and critical thinking skills, and virtual peer environments can provide social and emotional support. E-learning environments supported by AI can provide continuous and adaptive support to the learner in a way that is personalized and sensitive to individual differences. AI technologies for learner support can also extend to tools for instructors, helping them better understand the needs of individual learners and providing facilities for the instructors to help one another.

In social learning networks, adult learners are encouraged to be lifelong learners. Within this context, Hill introduced the concept of 'e-learning communities' where learners can join or leave a community at any time and where learning is an ongoing process. The effective use of social learning software can facilitate the establishment of such communities that are informal, not confined by time or location, and cater for a variety of learning situations. AI can be used in social learning networks to help learners find others with similar learning interests, form work groups, and locate resources that are relevant to their learning goals. The goal is to move beyond the 'one-size-fits-all' approach to education and deliver learning experiences that are more learner-centered.

8. Conclusion

The research ultimately concludes that the usage of AI in education has vast opportunities to improve today's education standards by providing a better learning experience for the learner. However, since AI development is a major long-term investment, it is crucial for today's educational institutions to consider its cost and benefits to decide whether the implementation of AI is feasible for improving the system. Even so, the researchers are optimistic that if AI development can continue advancing, it will be able to provide a better education even to the lower-class community.

In conclusion, it was clear that various researchers and developers have been focused on the utilization of smart systems, such as artificial intelligence (AI), in education. It may have started from the traditional form of e-learning with the provision of learning materials, to more sophisticated adaptive learning which caters to everyone’s needs based on their capability and competency. As AI's capability to mirror human cognitive and learning behaviour has been growing, it is not impossible that it will be utilized in a more complex system beyond what we can imagine today.