AI in Education: Transforming Learning and Teaching Methods

The application of Artificial Intelligence in education is set to change the way learning and teaching is done in the future (Holmes, Bialik, & Fadel, 2019). Conventional education systems involve well-defined syllabi, instructor-based learning, and actual classrooms. These are the basic constituents of traditional learning environments, which have been prevalent over the years. The main goal of the conventional system of education is to ensure that all students undergo similar learning experiences and cover all the necessary topics. This standardization is very important in ensuring that there is equity in education and also to ensure that there are clear learning expectations in different regions (Luckin, Holmes, Griffiths, & Forcier, 2016).

Traditional education systems are also characterized by teacher-centred approach in teaching. This approach focuses on the contact between the educators and the learners, which allows for timely feedback, individualized attention, and the acquisition of social skills. Traditional classroom environment gives the teacher an opportunity to physically direct the student and thus improve the learning process. Traditional classrooms remain the most common place where such interaction takes place and therefore helps to create a family atmosphere. These environments also provide for co-curricular activities including sports, arts and clubs among others which help in the overall development of the students and their physical, emotional and social well-being (Holmes, Bialik, & Fadel, 2019).

There are several benefits of traditional education systems such as organized class setting that supports discipline, time management and organizational skills most especially for young learners. The curricula are also general to ensure that every student is exposed to all subjects and gets a good foundation. In addition, traditional schools act as community centers where students, parents, and teachers can be active members of the society, which is an essential aspect of students’ social learning (Wang, Zhu, & Wang, 2020). However, traditional education systems are also associated with some drawbacks. The standardized model, however, does not consider the uniqueness of each learner and his or her learning profile, which results in some students disengaging and performing poorly. A major downside of traditional systems is that they are rigid in terms of schedules and curricula, which may not be suitable for students who learn differently or at different paces (Luckin et al., 2016). As a result, many public schools suffer from limited resources; they have large classes, old textbooks, and few support services, which can lead to poor academic achievement (West et al., 2020).

Traditional methods can also be very teacher oriented with emphasis on lectures, fewer activities that allow students to learn independently or in groups, and this can negatively affect the students’ critical thinking and problem-solving skills. Last but not the least, owing to the conventional testing and assessment methods, a student’s potential or growth may not be aptly measured as it does not involve application of knowledge but rather memorization (Piech et al., 2015).

However, based on the strengths and opportunities of traditional education systems, there are several important missing links in the current literature. The research gap that can be identified is the scarcity of longitudinal studies investigating the effects of traditional education approaches on the students’ achievement and their employment prospects. To this end, it is necessary to learn about the long-term impacts since these can help determine the extent of the effectiveness of traditional education (Holmes, Bialik, & Fadel, 2019). Thus, more research is required to find the best approach to serve the diverse students in traditional institutions, including learners with disabilities and those from disadvantaged groups. Inclusion is crucial to achieve fair education systems and practices today (West et al., 2020). Research on the evaluation of the effectiveness of teacher training and further professional development in achieving better results on the quality of education is scarce. Studies in this area can assist in establishing ways of how best to assist the educators. Also, further studies should be conducted on new forms of assessment that can give an overall picture of the learners’ knowledge and skills. Building on the work of Piech et al. (2015), enhancing the assessment tools will lead to improved ways of monitoring the students’ progress. More studies need to be done to determine the connection between parental and community participation and students’ performance and health. Involving families and communities is very important for students (Wang, Zhu, & Wang, 2020).

?Turning to these challenges and gaps in traditional education, AI in educational technologies, personalized learning systems, and automated grading systems can help. These innovations have the potential of changing the face of current learning models to make them better, efficient and inclusive. For proper utilization of these technologies and to successfully apply them, different business and research strategies can be adopted to formulate and solve the research issues related to them.

Disruptive Innovation and Jobs To Be Done (JTBD) Framework

One such strategy is the concept of Disruptive Innovation, coupled with the Jobs To Be Done (JTBD) framework. This approach can be used to identify unmet educational needs and develop AI-driven solutions that effectively penetrate and transform existing educational models (Christensen et al., 2016). Disruptive innovation refers to technologies or processes that initially cater to a niche market but eventually displace established competitors. In education, AI can act as a disruptive innovation by addressing specific needs that traditional systems fail to meet. For instance, AI can provide personalized learning experiences that adapt to the individual pace and style of each student. Traditional education often struggles with a one-size-fits-all approach, but AI can tailor educational content and methods to suit different learners, thereby enhancing engagement and outcomes. By focusing on underserved segments such as adult learners, remote learners, and students with special needs, AI-powered educational platforms can offer tailored content and interactive feedback, significantly enhancing learning outcomes. These platforms can adapt lessons in real-time based on student performance, providing immediate assistance and customized exercises to reinforce learning.

To overcome adoption barriers, robust support and training programs are essential. Technological accessibility remains a significant challenge, especially in under-resourced schools. Ensuring that both teachers and students are comfortable and proficient with AI tools requires comprehensive training programs. These programs should not only cover technical aspects but also pedagogical strategies for integrating AI into teaching. This holistic approach ensures that AI is not merely an add-on but an integral part of the educational process. Moreover, addressing teacher concerns about AI replacing their roles by emphasizing AI as a supportive tool rather than a replacement can aid in smoother adoption.

Blue Ocean Strategy Canvas

Another tool that can also help in the creation and sustainability of competitive advantages in non-competing markets within the education sector is the Blue Ocean Strategy Canvas. This approach helps organizations to leave the already saturated and competitive markets also known as red oceans and instead search for new markets and opportunities in the blue oceans (Kim & Mauborgne, 2005). In the context of education, this entails exploring and creating new markets for educational services where competition is low and innovation possibilities are immense.

For instance, AI applications in education can provide individualized training at a much lower price than human tutors, thus enabling more people to receive quality education. The Strategy Canvas allows for mapping out the landscape of education and determining where new value propositions can be generated by AI. One such area is AI-based formative assessments that offer feedback to students on a real-time basis as opposed to the conventional summative assessments. These assessments can assist students in knowing their performance and the aspects that require improvement in real-time, thus enhancing learning and teaching. With the Blue Ocean Strategy, educational institutions and AI developers can jointly create products and services that provide new value propositions, including tailored learning approaches and new forms of evaluation. This strategy not only increases the effectiveness of education but also decreases costs, thus allowing more people to afford advanced education. Similarly, by concentrating on developing new markets for AI in education, the approach does not directly compete with traditional systems, thus making the process more seamless.

Lean Canvas for Educational AI Startups

For those entrepreneurs who want to test business ideas and bring the educational AI startups to the next level, the Lean Canvas can be very helpful. This approach which is based on Business Model Canvas is used for startups to list down the assumptions, hypotheses, and to prototype and pivot based on the feedback received (Maurya, 2012). The Lean Canvas is a tool that enables startups to capture the key elements of their business model, and these include the problem, the solution, the metrics and the channels. In the case of the AI-based educational tools, the Lean Canvas can be applied to check assumptions regarding the impact of the tool, for instance, the effectiveness of adaptive learning and user’s engagement. For example, a startup may generate a proposition that AI increases student’s engagement due to the individual content approach. Subsequently, this provides the startup with information on the engagement levels of the target audience, the learning outcomes achieved, and the satisfaction of the users.

Likewise, the Lean Canvas also helps in the identification of risks and possible measures to prevent them. The main threats for educational AI startups are related to data privacy and technology integration problems. Through these risks, it is evident that if the startups can deal with these risks at the initial stage of the market research and development, they will be in a better position to develop more reliable and trustworthy AI tools. Real-time feedback and subsequent refinement of the product guarantee that the final product is most appropriate for the learners and tutors, hence increasing the chances of the product’s uptake and success in the market.

Action Research

Action Research can be used to study and solve real-life problems in educational environments and at the same time yield scholarly findings. This approach is known as action research and was first formulated by Kurt Lewin; it is a sequential process of planning, action, observation, and reflection for the purpose of addressing issues and enhancing practices (Lewin, 1946). AI can be incorporated in the teaching and learning processes through Action Research to address issues that are of concern in the education system for instance enhancing students’ participation or lightening the burden on tutors. Through involving the subjects of the study, including the teachers, learners, and other officials, Action Research helps in the development of AI solutions for actual use. For instance, a school may decide to incorporate an AI-based solution to monitor the students’ activities and their outcome. Thus, via successive rounds of evaluation, the tool could be enhanced according to the users’ suggestions and contribute to solving the engagement problems.

?Ethical issues are also well incorporated in Action Research as it is also concerned with the ethical implications of the activities it undertakes. This being the case, there is a need to ensure that the data collected and analyzed by AI on the performance of students is done in a right and proper manner. Thus, the stakeholders’ opinions can be engaged during the AI development and assessment to determine the possible ethical concerns and create recommendations for ethical AI application in education. This approach does not only increase the efficiency of the AI system but also increases the confidence of the users.

Pivot Strategy

The Pivot Strategy is vital for the educational AI startups to understand the need for a change in the strategy and make them to grow and become sustainable. A pivot is a radical shift in one or more of the propositions of the business model due to feedback and information (Ries, 2011). For the AI start-ups it may involve changing the target audience, changing the characteristics of the AI product, or even redefining the entire business model to fit the needs of the market. This implies that, using certain parameters called Key Performance Indicators (KPIs), one is able to determine when a change of direction is called for. For example, if a K-12 education focused AI tool did not get the desired usage, the startup might opt to shift all focus to the university or corporate training sector where there might be a bigger market for such tools due to the need for individualized learning. This paper has outlined how data-driven decision-making processes are useful in identifying the most suitable pivot strategies.

?A good pivot needs the organization to be willing to change and to be able to do so at the drop of a hat. It gives startups the ability to make changes and modifications to their products and services at any time depending on the environment and the available technologies. Thus, educational AI startups should be ready to adapt their products and services as well as listen to customers’ feedback to meet the challenges of the market.

Porter's Value Chain Analysis

By adopting the Porter’s Value Chain analysis, it is possible to identify the strategies that can be taken to improve the activities of the value chain and thereby the value of AI in education. The value chain analysis involves breaking down every activity that goes into the production of a good or service in order to establish areas that require enhancement (Porter, 1985). Thus, this analysis can benefit educational institutions and developers of AI in education by identifying ways to improve processes and services. Thus, studying the processes like content production, platform designing, and customer interaction, organizations can determine areas that can be benefited from AI. For instance, it is applied in producing content, creating customized materials for learning, and offering round-the-clock customer services through chats. These are not only more efficient, but they also make for a more connected, interesting experience for the students.

Thus, the integration of AI solutions into the educational process can create new value propositions that are difficult to imitate by competitors. Thus, by focusing on those areas that are most sensitive to the quality of the educational services, namely on the activities directly related to teaching and learning processes, as well as on the real-time assessment of student performance, the educational institutions can create competitive advantages which will allow them to offer the higher educational value.

Developing Compelling Value Propositions

Developing compelling value propositions is essential for educational institutions to attract diverse student populations and add value to their educational offerings: a value proposition is a statement that defines and outlines the reasons why a certain institution / product is important to the customer. For value propositions regarding AI in education, these should focus on the added value that can be derived from the application of these technologies in teaching and learning. Some of the possible application of AI in education include the development of individual learning plans, generating of real time data and delivery of adaptive learning. These abilities have the potential of improving the learning process and the level of students’ engagement. For instance, an AI-assisted study tool that can change the mode of delivery depending on the student’s ability and the pace of learning will enhance learning outcomes and retention. Real time analytics can help educators know the performance of students thus they can act at the right time.

It is vital to share these advantages with students, parents, and teachers to stand out in the crowded educational market. The marketing efforts should be placed on the quantitative gains of AI, including student achievement, participation, and support for the varied learners’ needs. Thus, focusing on the added value that AI can provide, educational organizations will be able to attract more students with different backgrounds.

Ethical Considerations and Challenges

As we move forward with implementing AI in education, it is crucial to consider the ethical implications and potential challenges. There is a need to ensure that students’ data are protected from being exposed and this requires the following; (Binns, 2018). It is therefore important that educational institutions put in place adequate measures in the management of the data of the students.

It is also necessary to consider the issues related to reinforcement of prejudice by AI or the role of AI in education (West, Whittaker, & Crawford, 2020). AI systems are as prejudiced as the data on which they are trained, hence if there are prejudices in the data, the AI systems may reinforce and even increase them.

Preventing this risk can be done by diversifying the training data of AI and applying fairness checks. Also, AI should be considered as an assistant and an extension of human teachers not their replacement. The discovered balance between the AI application and human to human interaction is paramount in the growth of the learners. Besides, the integration of AI into education is not without capital investments and personnel development. It is crucial that the educational institutions and the policymakers address the issue of access to AI for education to prevent the worsen of the digital divide. This involves acquiring relevant hardware and software such as laptops, computers, and ensuring that all students have access to the internet besides sensitizing teachers on how to incorporate AI in their teaching. There is also a need for further research on the effects of AI on learners’ achievements, cognitive growth, and employment preparedness (Holmes, Bialik, & Fadel, 2019). Longitudinal research can offer significant information on how AI works in education and the possible directions for the further progress and application. Despite having their merits, the conventional education systems have certain constraints that can be effectively resolved by integrating AI into the educational technologies. It is possible to build and deliver the AI solutions for education that will revolutionize the process and the outcome through utilizing such approaches as the Disruptive Innovation, Blue Ocean Strategy, Lean Canvas, Action Research, Pivot, Porter’s Value Chain, and Value Propositions. They have the potential of producing more student centered, effective and equitable learning environments that ready the students for the 21st century challenges. Although such a shift is possible and desirable, it must be done so wisely and taking into consideration the ethical issues, equity, and the need for future research and assessment. As we move forward, the goal should be to harness the power of AI to complement and enhance human teaching, rather than replace it, ultimately creating a more effective and accessible educational ecosystem for all learners.

References

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Kim, W. C., & Mauborgne, R. (2005). Blue Ocean Strategy: How to Create Uncontested Market Space and Make the Competition Irrelevant. Harvard Business Review Press.

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Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An Argument for AI in Education. Pearson.

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