Artificial Intelligence in Higher Education: A Comprehensive Guide

Artificial intelligence fundamentally changes the way we learn, how we teach, and who will be teaching in the future. AI is widely used in education for data-driven insights and personalized teaching. AI has the potential to solve some of higher education’s most complex conditions, such as student success and retention.

Highlights:

1. Artificial intelligence promises to make students’ educational experiences engaging and personalized.
2. Artificial intelligence can help educators identify patterns in data coming from student responses, which could help them address educational equity gaps.
3. Risks and challenges for AI in higher education include ethical concerns, privacy issues, and cost barriers.
   Institutions should map out an AI strategy to mitigate risks.

A new era of intelligent education is here. Artificial Intelligence (AI) has the potential to fundamentally change the way we learn, how we teach, and even who will be teaching in the future. AI’s disruptive impact on higher education challenges established norms and transforms our understanding of what it means to be a student or teacher. AI in Education is a new, multidisciplinary field that combines advances from many disciplines, including Psychology and Cognitive Science, Computer Science and Engineering, and Political, Social, and Educational Thought. These various fields produce a unique set of insights into the nature of intelligence, learning, and teaching. This dynamic sector shows great promise for advancing education today as well as solving some of the sector’s most complex conditions. AI in Education is particularly suited to the digital age and has the power to alter the very foundations of education, covering the full range of AI, from education analytics to machine learning to robotics in the classroom.

Colleges and universities are increasingly turning to AI in order to find the best possible solutions to complex challenges, such as enrollment management, student success and retention, and administrative workload. Neural Networks and Deep Learning are already widely used in the public education sector for data-driven insights and personalized teaching. Companies such as Knewton, which provides adaptive learning solutions to universities, and Pearson, which uses machine-learning algorithms to help students better understand content from online lectures, are already developing new technologies that effectively tailor education for individual learners. The new AI-based tools also have the potential to deliver faster and more accurate diagnostic tools, which help students of all ages better understand their learning strengths and weaknesses and allow them to continue studying efficiently.

The article offers an in-depth analysis of the benefits of AI for higher education, including student success, retention, enrollment, and resource management. The article also discusses examples of recent developments in AI technology and the benefits they can bring to learners. Moreover, the article addresses several challenges that AI will likely bring about in higher education, including ethical questions such as who owns student data and the need for more human-AI collaboration in higher education.

The Role of Artificial Intelligence in Higher Education

Artificial intelligence is increasingly used in higher education, from automating administrative tasks to providing students with personalized learning experiences. One of the most common ways AI is used in higher education is to provide students with customized learning experiences. By tracking students’ behaviors and interactions with course materials, AI can identify areas where they may need more help and provide them with targeted resources. For example, the adaptive learning platform Smart Sparrow uses AI to personalize learning content and activities for each student. As a result, students using Smart Sparrow have been shown to achieve better grades and retain more information than those who did not use the platform. Another way AI is used in higher education is to automate administrative tasks. This can free up staff time for important tasks, improving the institution’s efficiency. Specifically, AI can help institutions with a number of tasks and challenges, as presented below:

Enhancing and personalizing the learning experience for students

As education becomes more individualized and personalized, so must the methods used to teach it. Traditional methods of teaching large groups of students at once are no longer as effective as they once were. With the advent of AI, educators have a powerful tool at their disposal that can be used to enhance and personalize the learning experience for students. AI can be used in a number of ways to improve the learning experience for students. For example, it can create customized learning experiences for each student based on their specific needs and abilities. For example, using data from previous students’ performance, AI-powered systems can identify which topics are causing difficulty for individual students and provide targeted support. It can also be used to develop educational games and learning aids that are engaging and fun for students. By incorporating AI technology into their classrooms, universities can help students grasp complex concepts straightforwardly by making them engaging and entertaining. This is especially useful when creating new learning materials that cover certain topics for the first time in a class. For example, creating an interactive game around genetics can help students understand this complex topic more easily.

AI can also be used to provide real-time feedback to teachers on student performance, helping them identify areas where students may need additional support. Educators can utilize insights to provide personalized feedback on student progress and tailor their teaching methods accordingly. In addition, using AI in education can help schools reduce dropout rates and improve grades. AI-enabled student monitoring helps teachers identify at-risk students who need extra help or attention by tracking their attendance and academic performance over time. For example, the Chinese Ministry of Education announced that it would use AI to identify students who may be at risk of dropping out and provide them with resources to stay in school. Customizing the educational experience ensures that each student receives the best possible education. By using AI to help students stay on track with their coursework, universities can boost student retention and increase overall graduation rates.

Developing insights about student needs and challenges

Higher education institutions can improve their academic programs by responding to student needs and challenges. One way AI is used in higher education is to gather data about student needs and challenges. This data is used to improve academic programs so that they better meet the needs of students. For example, by tracking which students are struggling with certain concepts, AI can help identify areas where instructional materials need to be improved. AI can also be used to assess which students are at risk of dropping out or not completing their degrees so that interventions can be put in place to support them. AI enables universities to better understand the effectiveness of academic programs by enabling data-driven decision-making that provides real-time, personalized insights into each student’s progress through the program.

Moreover, AI-driven instructors can help students determine what classes will be most useful for the next year by looking, again, at their strengths and weaknesses relative to the coursework offered. This technique can help reduce student anxiety about deciding which courses to enroll in each semester. Finally, AI can also be used to manage data about students’ academic progress in order to ensure that no individual students are falling through the cracks. AI algorithms can identify students who may be at risk of dropping out so they can receive additional support. AI is also used as a tool for providing 1:1 advising and helping students choose classes that are better suited for their interests, skills, and academic track.

Helping faculty with research and instruction

AI has the potential to help faculty with both research and instruction. When it comes to research, AI can be used to gather and analyze data, identify patterns, generate insights and make predictions that can help inform decision-making. In particular, AI is used to help identify patterns and trends in data sets, as well as to generate new hypotheses for further investigation. Additionally, AI can be used to create virtual assistants that help researchers with tasks such as data collection and analysis. With the recent increase in data volumes and computing power, AI can support researchers in extending their research timelines. For example, researchers may need to gather data from many sources using different methodologies and then analyze the data before making a conclusion. Using AI, researchers can quickly and efficiently search through vast amounts of data, identifying patterns or anomalies that lead to novel discoveries. For instruction, AI can be used to create personalized learning experiences, provide real-time feedback, and assess student understanding. AI is being used to assist educators in creating new instructional materials and providing feedback on the effectiveness of these materials. Instructors can also use AI to schedule activities and help create productive learning environments for students. The next generation of students will require practical experience to succeed, which can be accomplished using AI to transform traditional educational materials into interactive lessons delivered through virtual reality/augmented reality or other platforms that provide immersive experiences. AI can help educators identify patterns in student performance and suggest steps for improvement based on specific goals that have been set.

Alleviating administrative and teaching loads for faculty members

One of the most important, yet far underutilized, applications of AI in higher education is its potential to alleviate administrative and teaching loads for faculty members. In particular, machine learning can be used to automate routine tasks such as grading, providing feedback on student papers, and managing course logistics. By augmenting human teachers with AI, educators can improve the quality of their instruction, freeing up time to focus on research, mentoring students, and developing new courses. Using AI to automate administrative functions will also create new opportunities for faculty members to focus on teaching. For example, course logistics is currently a major source of delays and bottlenecks in the educational process. Schools must manually schedule classes each semester and match instructors with course sections. In addition, students wait for decisions about their enrollment or visa status, which can take weeks or months. The risk of human error is high, and the cost of employing administrators dedicated to the task. Machine learning algorithms are good at automating logistical tasks that involve routine decision-making based on data such as class schedules, syllabi, grade distributions, and student feedback.

Managing institutional data for operations and resources optimization

The ever-growing field of AI is revolutionizing the way we manage data and resources in higher education. AI algorithms analyze institutional data to provide insights that optimize operations and resources. This includes improving how a school allocates faculty, staff, and financial resources by better understanding how these resources are used. AI in higher education is also used for predictive analytics, forecasting enrollment and academic demand, and making recommendations. AI can also assist in discovering other areas for continuous improvement. AI-powered technology can help schools automate processes, reduce operational costs, and save time, equating to more efficiency and fewer student issues. For example, AI can be applied to the admissions department to identify potential students who will succeed based on their academic record, grade point average, and test scores. This information can help admissions officers recruit promising students with positive potential. One example of an institution using AI is Texas Tech University. The university uses predictive analytics to optimize the recruiting process by identifying top candidates from incoming students.

Reducing administrative costs and improving organizational efficiency

AI can also be used to streamline administrative processes in higher education institutions. For example, it can be used to automate tasks such as scheduling, tracking student progress, and managing records. By automating tasks such as schedule creation, course registration, and transcript management, AI can free up staff time for strategic and tactical tasks. For example, an AI-enabled system can play a role in helping institutions better understand the costs of providing educational services and then predict how costs might vary based on different scenarios (e.g., adding new programs or increasing enrollment). This information can help provide better planning information and improve the ability to forecast expenses. It will also facilitate a robust analysis of multiple operating models across campus and enable cost/benefit analysis of potential initiatives or service lines without requiring additional staff time. In addition to helping institutions better understand their cost structure, collaborative decision-making can also improve productivity. AI-enabled systems can help colleges and universities make better day-to-day decisions through improved access to the correct data at the right time. This is particularly important in areas such as research and development, where there is an abundance of available sources of data, which are often not utilized entirely or in a timely manner. An AI system could help an institution capture relevant data from an array of disparate sources and then translate that information into valuable visualizations or even create new insights by synthesizing information from numerous sources.

Deploying Artificial Intelligence in the Classroom

The world of higher education is evolving. With the rise of AI, educators are beginning to use intelligent systems to enhance teaching and learning. AI is used in a variety of ways to improve the educational experience for students and educators alike. Here below are only a few potential scenarios where AI could be applied in teaching and learning:

Developing individualized learning plans

AI can be used to develop individualized learning plans for each student based on their unique strengths, weaknesses, and interests. This would allow for personalized and effective education for each student. The plan can be updated throughout the year to reflect changes in learning. For example, a student who is struggling with math might need extra help and encouragement to complete their assignments. Their AI-supported instructor could adjust the lessons or assignments to focus on areas that need improvement. Likewise, AI can be used to integrate different curricula into individualized plans for each student. Students have unique interests and strengths, so their curriculum should also be customized to match their own interests and needs. An individualized learning plan can be developed for each student based on their strengths and weaknesses, as well as an integration of other subjects they want to learn about.

Furthermore, assessment data, such as test scores, grades, attendance, coursework, and peer evaluations, are commonly used to establish a baseline of student knowledge. The next step is to use that data to generate individualized assignment plans. This can be done using machine-learning models designed around personal interests, skills, and affinities. An effective assessment will provide:

• information about how students are adapting to new concepts,
• information about whether students have mastered individual skills,
• information about where each student is and how they are progressing,
• suggestions for additional resources and learning methods,
• information about how students will apply their knowledge in real-world settings,
• information about what students need to accomplish for the next learning objective, and so on.

Adaptive learning

The term ‘adaptive learning’ is used to describe a range of technology-based approaches that aim to individualize instruction to meet the needs of each learner. This type of technology is being used by some of the biggest names in online education, such as Khan Academy and Coursera. Adaptive learning systems use data from students’ interactions with digital content and tools to constantly adjust the difficulty level, content, and pace of instruction. The goal is to provide every student with a personalized learning experience that is optimally challenging and engaging. A growing body of evidence suggests adaptive learning can improve student outcomes. For instance, a meta-analysis of 49 studies found that adaptive learning interventions resulted in significantly higher scores on standardized tests, as well as increased grades and course completion rates (Nunes et al., 2022).

Automating the classroom experience

With the ever-growing popularity of online and distance learning, many colleges and universities look for ways to automate the classroom experience. One way they do this is by using AI to create digital assistants that can help with tasks like grading papers and providing feedback to students. Various AI tools are used in higher education, including chatbots, natural language processing, and machine learning. Chatbots can be used to answer student questions or help them navigate the college website. Natural language processing can be used to grade essays or provide feedback on writing assignments. With the increasing use of AI in higher education, some professors have experimented with automated grading. While this may sound like a daunting task, several software programs can handle the grunt work of grading. One such program is GradeScope, which was designed to automate grading for large courses. GradeScope supports a variety of assignments, including multiple-choice, short-answer, and programming assignments. Finally, machine learning can be used to create personalized learning experiences for each student.

Creating Virtual Reality Experiences

AI can be used to create virtual reality experiences that simulate real-world situations. This would allow users to engage in immersive, effective learning experiences and improve the overall user experience. This can be further enhanced by applying natural language processing to virtual reality training simulations to provide necessary feedback and eventually create a seamless experience between AI-generated virtual experiences and the real world.

For more information on the prospects and impact of virtual reality in higher education innovation, read “A New Dawn: Technology and Educational Innovation in the Age of Pandemic.”

Enhancing Online Learning

AI can be used to enhance online learning experiences by creating personalized and engaging online learning experiences for students and providing real-time feedback and personalized content recommendations. This could make online learning effective and efficient, ensuring students get the most out of their online courses.

Risks and Challenges of Artificial Intelligence in Higher Education

Artificial intelligence in higher education is rapidly increasing as universities seek to gain a competitive edge in the global market. However, many risks and challenges associated with AI must be considered before full-scale implementation. These include:

Accessible and affordable AI technologies

There are concerns that AI will exacerbate inequalities in education, with wealthy institutions and students benefiting the most. To ensure that AI technologies are accessible and affordable for all institutions, including those in developing countries, it is essential to:

• Make sure that open source solutions are available and well supported.
• Invest in research and development of new AI technologies that are tailored to the needs of developing countries.
• Work with governments and international organizations to promote policies that enable the use of AI in education in developing countries.

Ethics and AI technologies

As AI technologies become more sophisticated, it is essential that they are developed and used in ethically responsible ways that protect the privacy of individuals and the confidentiality of data. A number of ethical considerations need to be taken into account when using AI in higher education. These include:

• Ensuring that data is collected and used ethically ensures that consent is obtained by organizations before people’s data is collected and used for academic purposes.
• Protecting the privacy of individuals – for example, by ensuring that personal data is not shared without consent or used for purposes that the individual has not agreed to.
• Confidentiality of data ensures that confidential information such as exam results or medical records is not accessed or released without proper authorization.
• Avoiding bias – for example, ensuring that AI technologies are not used in a way that could discriminate against individuals or groups of people.

  

By taking these ethical considerations into account, higher education institutions can ensure that they are using AI in a responsible and transparent way. The absence of a framework for ethical AI raises the risk that AI may be misused, leading to the erosion of public trust in AI systems and, potentially, unintended economic consequences. With this in mind, higher education institutions must develop and implement an informed approach to AI. By creating a set of guidelines that address the ethical implications of AI, higher education institutions can promote trust and transparency as well as ensure that their use of AI does not put them or their students at risk. The ethical guidelines for the use of AI in higher education institutions should address nine key issues:

1. Data Collection and Usage: collection, storage, management, transfer, ownership, and sharing of data will have a major bearing on the implementation of AI. The guidelines should address all these aspects and articulate university policies to guide the responsible use of data.
2. Adverse Impacts: the institutions must consider the potential adverse impacts of applications and make appropriate decisions about what constitutes acceptable risks. In addition, they must develop policies on handling extreme risks or unintended consequences that may arise from the implementation of AI programs.
3. Transparency: credible algorithms used in educational systems should be openly disclosed to ensure there is no unfair discrimination or bias. In addition, transparency of data sets and their underlying meaning should be communicated to ensure that the AI is used for legitimate purposes only.
4. Ethics: educational organizations are expected to develop ethical policies on the use of AI in education. For example, some universities may choose to prohibit the use of AI for matters related to admissions and hiring decisions. They are responsible for providing their students with the best possible learning experiences, considering concerns about fairness, privacy, and safety.
5. Monitoring: educational institutions should develop processes for monitoring and assessing those programs using AI techniques; i.e., they should work with experts to monitor how these programs function, evaluate outcomes and ensure that there are no unintended biases or negative consequences.
6. Data Privacy and Security: Educators should consider how they will protect students’ privacy while leveraging information gleaned from data collected through AI programs or analytics.
7. Legal Implications: Educators should consider the legal implications of AI-based tools used in their schools, including potential copyright and fair use concerns, as well as potential liabilities associated with algorithms and bias or discrimination in their data.
8. Ethical Considerations: Educators should consider ethical issues associated with the use of AI in classrooms, including potential effects on students’ perception of fairness and trust in technology as well as effects on students’ learning and cognitive development.
9. Accountability: Educators should consider how they will measure and evaluate AI tools used in their schools to ensure their use is truly effective for supporting student learning. As classrooms become more digitally literate, schools will need to develop policies and practices that recognize the benefits and address the risks of AI technologies.

Inclusive use of artificial intelligence technologies

While there are various ways that AI can be used in higher education, one of the most important is its potential to create new opportunities for all learners, including those with disabilities and diverse learning needs. For example, iversity, a German MOOC platform and one of the first companies to offer fully inclusive MOOCs uses AI to automatically correct spelling and grammar errors in students’ written work. The software ‘understands’ what the student intended to write and makes suggestions for alternative words. At its simplest, AI can provide positive support for students who struggle with writing by helping them avoid making common mistakes or giving them suggestions for alternatives. In addition to correcting errors using AI, there are also other ways that intelligent systems could help learners with disabilities improve their performance in a number of areas. For example, assistive technologies include hardware or software that people with disabilities use to increase the accessibility of computers and other devices to enable them to be used by people with disabilities. For instance, hardware such as alternative keyboards and special mice can help users with disabilities interact with these systems.