Artificial Intelligence Tutoring in Business Education: Implications for Marketers and Managers

Abstract

This literature review explores the integration of artificial intelligence (AI) tutoring in business education and its potential impact on marketers and managers of educational institutions. It examines the historical progression of AI in education, its theoretical underpinnings, and the technological structures facilitating personalized and adaptive learning. Through case studies and empirical data, it illustrates how AI tutoring improves student performance, streamlines operations, and offers strategic benefits for marketing and management. Marketers can benefit from targeted marketing strategies, enhanced brand positioning, and market distinction, while managers can achieve operational efficiency, optimized resource allocation, and data-driven decision-making. However, ethical considerations such as data privacy, algorithm fairness, and human oversight are also discussed.
Keywords: Artificial Intelligence, Business Education, AI Tutoring, Marketing, Management, Operational Efficiency.
Jel Code: M31

1. Introduction

Higher education institutions are facing unprecedented challenges in meeting the diverse needs of learners in a rapidly changing world (Hajeer et al., 2023). Traditional teaching methods often struggle to provide personalized instruction and adapt to individual learning styles (Hajeer, 2024). Additionally, institutions face pressure to optimize resources, streamline operations, and make data-driven decisions to remain competitive in the education market. Artificial intelligence (AI) tutoring has emerged as a promising solution to address these challenges. By utilizing advanced technologies such as machine learning, natural language processing, and educational data mining, AI tutoring systems can provide personalized learning experiences, automate administrative tasks, and offer valuable insights for institutional decision-making (Chaudhry & Kazim, 2022).

The present literature review explores the integration of AI tutoring in business education and its implications for marketers and managers of educational institutions. The review begins by defining AI tutoring and tracing its historical development, highlighting the evolution from early rule-based systems to sophisticated adaptive platforms. It then delves into the theoretical foundations of AI tutoring, drawing on educational theories such as constructivism, cognitivism, and behaviorism, and examines the technological frameworks that underpin AI tutoring systems. The review further explores the diverse applications of AI tutoring in business education, showcasing case studies, curriculum integration, and the impact on student outcomes.
Building on these findings, the review discusses the implications of AI tutoring for marketers and managers. For marketers, AI offers opportunities to enhance brand positioning, differentiate educational offerings, and implement targeted marketing strategies. Managers can leverage AI to improve operational efficiency, optimize resource allocation, and make data-driven decisions. However, the review also acknowledges the ethical considerations associated with AI implementation, including data privacy, algorithmic bias, and the need for human oversight. Finally, the review concludes by outlining best practices for adopting AI tutoring systems and identifying future research opportunities in this rapidly evolving field.

2. Background and Context

AI tutoring refers to the use of artificial intelligence technologies to simulate human tutoring and provide personalized learning experiences for students (Mitra et al., 2021). This involves leveraging machine learning algorithms to analyze student data, identify knowledge gaps, and tailor instruction and feedback accordingly. Natural language processing allows AI tutors to interact with students in a natural and engaging way, providing explanations and answering questions in a way that mimics human tutors. Intelligent tutoring systems (ITS), defined by Woolf (2009) as „computer systems that provide immediate and customized instruction or feedback to learners,” are a prominent example of AI tutoring applications, providing customized instruction and feedback to students, simulating a human tutor’s role.

The concept of AI in education dates back several decades, with early systems focusing on simple programmed instruction and rule-based tutoring. For example, the SCHOLAR system developed in the 1970s (Carbonell, 1970) was an early attempt to use AI for tutoring in geography. Over time, advancements in AI technologies have led to the development of more sophisticated and adaptive tutoring systems. For instance, Woolf (2009) discusses the evolution of intelligent tutors from simple feedback systems to complex, student-centered learning environments. The integration of AI in education has progressively moved from theoretical research to practical applications, with significant milestones marked by the development of adaptive learning platforms and AI-driven educational tools.

Currently, AI is widely used in various aspects of business education, from enhancing classroom learning to optimizing administrative functions. AI tutoring systems are employed to provide personalized learning experiences, adapting content and instructional methods to meet individual student needs. These systems are designed to identify students’ strengths and weaknesses, offering tailored support to improve learning outcomes. For example, Baker and Inventado (2014) discuss the use of AI-powered tutoring systems to address the diverse learning needs of business students and provide personalized feedback and guidance. Hall (2020) highlights the use of web-based intelligent tutors in management education, which not only enhance learning performance but also prepare students for the global marketplace. Additionally, AI is used in administrative tasks such as grading, attendance tracking, and providing immediate feedback, thereby freeing up time for educators to focus on more complex instructional tasks.

AI’s role in business education also extends to marketing and management. According to Elhajjar, Karam, and Borna (2020), AI enhances students’ abilities, skills, and marketability, making them more attractive to potential employers. The use of chatbots and virtual assistants in student recruitment and support is another example of how AI is transforming marketing and management in educational institutions (Dwivedi et al., 2020). AI’s capability to analyze large datasets and predict trends allows marketers to design targeted campaigns and improve student recruitment efforts.

The current state of AI in business education reflects a growing trend towards integrating advanced technologies to create more dynamic and responsive learning environments. The potential of AI to revolutionize education is significant, as it offers innovative solutions to longstanding challenges in teaching and learning. However, it is essential to address the ethical considerations and challenges associated with AI implementation, such as data privacy, potential biases, and the need for transparency and fairness in AI-driven educational systems.

3. Theoretical Foundations in Education and Technology

AI tutoring in business education is grounded in several educational theories, particularly constructivism and cognitivism, along with elements of behaviorism. Constructivism emphasizes the active role of learners in constructing their own understanding and knowledge through experiences and interactions. This aligns with AI tutoring’s ability to provide tailored experiences that empower learners to take ownership of their learning journey (Woolf, 2009). This theory posits that learners build new knowledge upon the foundation of previous learning, thus engaging in a continuous process of hypothesis testing and cognitive restructuring (Gupta, 2011). Constructivist approaches in AI tutoring facilitate personalized learning environments where learners interact with the material, receive immediate feedback, and adjust their understanding based on the AI’s responses. Cognitivism, however, focuses on the mental processes involved in learning, such as attention, memory, and problem-solving. AI tutoring systems utilizes cognitive principles by presenting information in ways that align with how the human mind processes information (Aleven et al., 2016). Behaviorism, on the other hand, focuses on observable behaviors and the ways they are learned and reinforced through interaction with the environment. This theory is often applied in AI tutoring through reinforcement learning algorithms, which provide immediate rewards or corrections to shape student behavior and learning patterns (Gupta, 2011). For instance, intelligent tutoring systems (ITS) often use behaviorist principles to provide repetitive practice and reinforcement, helping students learn specific skills through incremental learning steps (Koedinger, Corbett, & Perfetti, 2012).

The implementation of AI tutoring systems relies on sophisticated technological frameworks and algorithms that assist tailored and adaptive learning practices. One fundamental aspect is the use of machine learning algorithms, which enable AI systems to learn from data and improve their performance over time. These algorithms analyze student interactions, identify patterns, and adjust instructional strategies accordingly (Aleven, McLaughlin, Glenn, & Koedinger, 2016). For example, learning algorithms can be trained on large datasets of student interactions to predict the most effective instructional responses, while reinforcement learning algorithms can adapt to each student’s learning pace and style. Another critical component is natural language processing (NLP), which allows AI tutors to understand and respond to student queries in natural language. NLP techniques enable AI systems to interpret the semantic meaning of student inputs, provide relevant feedback, and guide students through complex problem-solving processes. This interaction simulates a human tutor’s role, making the learning experience more engaging and intuitive for students. Additionally, cognitive models play a significant role in AI tutoring systems. These models represent the knowledge structures and cognitive processes that underlie student learning. By mapping out the various cognitive steps involved in problem-solving, AI tutors can provide targeted assistance that addresses specific misconceptions and knowledge gaps (Koedinger et al., 2012). For example, cognitive task analysis can help break complex tasks into smaller, manageable components, allowing AI tutors to offer step-by-step guidance and scaffolded support. AI tutoring systems also incorporate educational data mining techniques to analyze vast amounts of educational data and gain insights into student learning behaviors and outcomes. This data-driven approach allows institutions to continuously refine their AI tutoring strategies for optimal effectiveness (Luckin, Holmes, Griffiths, & Forcier, 2016). These insights can inform the development of more effective instructional strategies and adaptive learning paths (Aleven et al., 2016). For instance, clustering algorithms can group students with similar learning patterns, enabling AI tutors to provide group-specific interventions and support.

The integration of constructivist, cognitivist, and behaviorist educational theories with advanced technological frameworks and algorithms forms the foundation of AI tutoring systems. These systems leverage machine learning, NLP, cognitive modeling, and educational data mining to create personalized, adaptive, and effective learning experiences. By continuously evolving and adapting to individual learner needs, AI tutoring systems may significantly enhance business education and address the varied challenges faced by marketers and managers in educational institutions.

4. Applications in Business Education

AI tutoring has been applied in business schools, providing insights through several case studies. One example is a case-based reasoning intelligent tutoring system (CBR-ITS) implemented in a business school setting. This system utilized AI to suggest personalized learning materials based on real-time student data. Students who received personalized lessons through this system demonstrated significantly better performance compared to those who received non-personalized content (Masood & Mokmin, 2017). This case study supports the broader recognition in the literature that AI-driven personalization is a key factor in enhancing student engagement and learning outcomes (Woolf, 2009; Chaudhry & Kazim, 2022). Another example is an AI-based case study for accounting students. This project integrated AI and machine learning techniques into the accounting curriculum, allowing students to improve their decision-making skills by analyzing the business value of AI-driven projects.

The case study demonstrated that students developed better analytical, interpretative, and communicative skills, which are crucial for their professional growth (Todorova & Bogdanova, 2021). This case not only illustrates the potential of AI to bridge the gap between theoretical knowledge and practical application in business education but also aligns with the growing demand for AI-literate professionals in the business world, as highlighted by Chan and Lee (2023).
Integrating AI tutoring in business courses has led to significant changes in curriculum design. AI tools are being embedded into various business courses to provide personalized and adaptive learning experiences. For instance, AI-based e-tutorials have been incorporated into undergraduate and postgraduate courses to enhance digital literacy and support blended learning environments (McGuinness & Fulton, 2019). Students appreciated the flexibility and accessibility of these e-tutorials, which allowed them to review concepts at their own pace and reinforced classroom learning (McGuinness & Fulton, 2019). AI has facilitated the development of hybrid human-AI tutoring models in business education. A study conducted across multiple urban schools implemented AI-assisted tutoring alongside human tutoring in business courses. The findings indicated that students engaged with the AI tutor achieved higher proficiency levels and better overall performance, particularly those from lower socio-economic backgrounds (Thomas et al., 2023). This demonstrates the potential of AI to enhance student learning and addresses the ethical imperative of ensuring fair access to AI-powered educational resources, as highlighted by Nguyen et al. (2023).

4.1 Student Outcomes

The impact of AI tutoring on student performance and learning outcomes seems to have been considerable. AI tutors have been shown to improve academic performance by providing personalized feedback and adaptive learning pathways. For example, in a neuroscience course, students using an AI tutor that employed principles such as spaced repetition and retrieval practice achieved significantly higher grades compared to their peers who did not use the AI tutor (Baillifard et al., 2023). This personalized approach helps students to better grasp key concepts and retain information over time. This finding supports the broader literature on AI in education, which suggests that personalized learning experiences can lead to improved learning outcomes (Chaudhry & Kazim, 2022).

Additionally, AI tutoring systems have been effective in identifying at-risk students and providing timely interventions. AI tools can analyze student data to detect patterns indicative of potential academic difficulties, enabling educators to offer targeted support. This proactive approach helps improve retention rates and ensures that students receive the necessary assistance to succeed (Koedinger et al., 2012). The literature examined in this section shows that the integration of AI tutoring in business education has led to enhanced learning experiences, improved academic performance, and greater student engagement. Utilizing advanced AI technologies, business schools can offer personalized, adaptive, and effective educational solutions that serve the different needs of their students.

5. Implications for Marketers

For their marketing strategy, educational institutions can use AI tutoring to attract and retain students by employing targeted and data-driven marketing strategies. AI-powered tools can analyze massive amounts of data to identify potential students, understand their preferences, and tailor marketing campaigns accordingly. For example, AI can analyze student demographics, academic interests, and online behavior to create personalized marketing messages that resonate with individual learners (Zawacki-Richter et al., 2019). This level of personalization can significantly increase engagement and conversion rates, making AI a valuable asset in student recruitment. Additionally, AI can enhance customer relationship management (CRM) systems by providing insights into student interactions and behaviors, enabling institutions to engage with students more effectively and address their needs promptly. AI algorithms can generate personalized content and recommend the best times to post on social media, ensuring maximum engagement. For example, AI-driven chatbots can provide instant responses to prospective students’ inquiries, enhancing their experience and increasing the likelihood of enrollment (Zawacki-Richter et al., 2019). Moreover, predictive analytics can forecast trends and student behaviors, helping marketers to design proactive strategies that anticipate student needs and preferences. This proactive approach to student engagement can promote a sense of community and belonging, leading to increased student satisfaction and loyalty (Liu et al., 2021).

5.1 Brand Positioning and Differentiation

The integration of AI tutoring systems can enhance an institution’s brand and reputation. By adopting advanced AI technologies, educational institutions can position themselves as innovative leaders in the education sector. This is particularly relevant in the context of business education, where innovation and technological proficiency are highly valued by prospective students (Hall, 2020). Institutions that use AI to offer personalized and adaptive learning environments can differentiate themselves from competitors, thereby enhancing their brand value (Sood & Pattinson, 2023). AI can also contribute to academic excellence, which is a crucial aspect of brand positioning. Institutions that implement AI tutoring systems often see improvements in student performance and satisfaction. These positive outcomes can be highlighted in marketing materials to showcase the institution’s commitment to providing high-quality education (Bilad et al., 2023). Furthermore, success stories and testimonials from students who have benefited from AI tutoring can be used to build a strong, trustworthy brand image. This focus on student success and positive outcomes can resonate with potential students and their families, who are seeking institutions that can deliver tangible results.

AI tutoring can provide a significant competitive edge in the education market by offering unique educational experiences. Institutions that adopt AI tutoring systems can offer personalized learning paths that serve individual student needs. Moreover, AI can help institutions to streamline administrative processes, such as grading, attendance tracking, and student support, making operations more efficient and cost-effective. These efficiencies can translate into cost savings that can be passed on to students in the form of lower tuition fees or reinvested in other areas to further enhance the educational experience (Chhatwal, Garg, & Rajput, 2023). Institutions can also use AI to enhance online and blended learning environments, which are increasingly in demand. By offering flexible and adaptive learning options, institutions can appeal to a broader audience, including working professionals and international students (George & Wooden, 2023). AI can also enable institutions to stay ahead of industry trends by continuously developing their educational offerings. For example, AI-driven insights can help institutions to identify emerging skills and knowledge areas that are in high demand in the job market, allowing them to update their curricula accordingly. This responsiveness to market demands can be a powerful selling point for institutions, as it demonstrates their commitment to preparing students for the future of work (Silva & Janes, 2020).
To conclude this section, strategic implementation of AI tutoring systems can improve marketing strategies, brand positioning, and market differentiation for educational institutions. By utilizing AI, institutions can attract and retain students, build a strong brand, and gain a competitive edge in the education market. As AI technologies continue to advance, their potential to transform education and increase institutional success is expected to increase.

6. Implications for Managers

AI tutoring systems have the potential to streamline various educational management processes, thereby enhancing operational efficiency. One significant advantage of AI is its ability to automate routine tasks such as grading, attendance tracking, and administrative paperwork. By utilizing AI, institutions can reduce the workload on educators and administrative staff, allowing them to focus more on strategic activities and direct student engagement (Chhatwal, Garg, & Rajput, 2023). For instance, AI-powered chatbots can handle routine student inquiries, freeing up staff to focus on more complex issues, and AI-driven systems can automate the scheduling and delivery of personalized feedback, improving the student experience and reducing administrative overhead. AI also enhances data management and analysis capabilities. Educational institutions generate vast amounts of data, which can be challenging to manage and analyze manually. AI tools can efficiently process and analyze this data to provide insights into student performance, learning patterns, and institutional operations. These insights can be instrumental in identifying areas for improvement in course design, teaching methodologies, and student support services (Luckin, Holmes, Griffiths, & Forcier, 2016). Furthermore, AI-driven systems can monitor and predict student needs, enabling proactive interventions to support student success and retention.

The implementation of AI tutoring systems has significant implications for resource allocation in educational institutions. AI can help optimize staffing by identifying areas where automation can replace or complement human labor, thereby reducing the need for additional staff and lowering operational costs. For instance, AI-powered virtual tutors can handle basic tutoring tasks, allowing human instructors to focus on more complex concepts and individualized mentorship, as suggested by Roll and Wylie (2016). This not only saves costs but also ensures that human resources are utilized where they are most needed, such as in complex problem-solving and personalized mentoring. In terms of physical resources, AI can aid in the efficient management of facilities and infrastructure. AI-driven systems can monitor the usage of classrooms, labs, and other facilities, providing real-time data on occupancy and utilization rates. This information can help managers optimize the scheduling of classes and events, ensuring that facilities are used to their full potential and reducing downtime (Sastry, 2007). This optimization can lead to cost savings and a more efficient use of space, benefiting both the institution and its students.

AI tutoring systems provide a wealth of data that can significantly enhance managerial decision-making processes. By collecting and analyzing data on student performance, engagement, and learning outcomes, AI can provide managers with actionable insights to improve educational practices and policies. For example, AI can identify patterns and trends in student behavior, such as which topics students find most challenging or which teaching methods are most effective (Cukurova, Kent, & Luckin, 2019). These insights can inform curriculum development, pedagogical approaches, and student support services, ultimately leading to a more effective and responsive educational environment. Furthermore, AI can support strategic planning by predicting future trends and outcomes. Predictive analytics can forecast enrollment numbers, graduation rates, and other key performance indicators, helping managers to set realistic goals and develop long-term plans. AI can also simulate different scenarios, allowing managers to assess the potential impact of various strategies and make informed choices (Wang, 2020). This ability to model and anticipate future scenarios can be valuable for strategic decision-making in the face of uncertainty and change. AI also assists real-time decision-making by providing up-to-date information. For instance, AI systems can monitor student progress and flag at-risk students who may need additional support. This allows managers to intervene promptly and provide targeted assistance, improving student retention and success rates (Holstein & Aleven, 2021). Additionally, AI can help managers to maintain high standards of academic integrity by detecting instances of plagiarism and other forms of academic bad behaviour. This practical approach to addressing academic integrity issues can help protect the institution’s reputation and ensure a fair learning environment for all students.

7. Conclusions

This literature review explored the integration of AI tutoring in business education and its implications for marketers and managers. Key findings highlighted that AI tutoring enhances educational outcomes by providing personalized learning experiences and adaptive learning paths. The historical development of AI in education shows a progressive improvement from simple automated systems to sophisticated AI-driven tools capable of individualized instruction and real-time feedback (Woolf, 2009; Aleven et al., 2016). Current trends indicate a significant acceptance of AI in business schools, where AI tutors are being used to enhance digital literacy, support blended learning, and provide personalized assistance to students (Elhajjar, Karam, & Borna, 2020). These trends align with the growing recognition of AI’s potential to revolutionize education and cater to the diverse needs of learners in the digital age (Chaudhry & Kazim, 2022).

For marketers, AI tutoring systems offer a competitive edge by enhancing the institution’s brand and attracting students. AI’s ability to personalize learning experiences and improve academic outcomes can be leveraged in marketing strategies to highlight the institution’s commitment to innovative education (Sood & Pattinson, 2023). This can be particularly appealing to prospective students who value tailored learning and seek institutions that are at the forefront of educational technology. For managers, AI tutoring systems streamline operational processes by automating routine tasks and providing data-driven insights for decision-making. This can lead to more efficient resource allocation, better academic performance tracking, and improved student support systems (Chhatwal, Garg, & Rajput, 2023; Baumgart & Mamlouk, 2022). Additionally, the cost-effectiveness of AI tutoring can be a significant factor in attracting budget-conscious students and their families.

7.1 Best Practices and Research Opportunities

Educational institutions looking to adopt AI tutoring systems should consider several best practices. Firstly, integrating AI should be done gradually, starting with pilot programs to assess effectiveness and gather feedback. This allows institutions to make necessary adjustments before a full-scale rollout (Mourali et al., 2021). Training educators and administrative staff on how to use AI tools effectively is also crucial. This includes understanding how to interpret AI-generated data and integrate it into teaching and management practices (Sastry, 2007). Institutions should also establish clear policies for data privacy and security to protect student information. Regular audits and updates of AI systems are necessary to ensure they remain unbiased and effective (Chhatwal, Garg, & Rajput, 2023).

Despite the advancements in AI tutoring systems, there are still gaps in the literature that present opportunities for future research. One area is the long-term impact of AI tutoring on student outcomes and career success. Longitudinal studies could provide insights into how AI-assisted learning influences graduates’ performance in the job market (Elhajjar, Karam, & Borna, 2020). Another area for research is the ethical implications of AI in education, particularly concerning data privacy and algorithmic bias. Exploring how different institutions manage these challenges can offer best practice frameworks for others to follow (Wang, 2020). Additionally, research could focus on the cost-benefit analysis of implementing AI systems in educational settings, providing a clearer picture of the financial implications for institutions (Baumgart & Mamlouk, 2022). Furthermore, research could investigate the potential of AI tutoring to address specific challenges in business education, such as developing leadership skills, fostering creativity, and preparing students for the evolving demands of the global marketplace.

7.2 Ethical Implications and Final Thoughts

Deploying AI in education raises several ethical considerations. Privacy and data security are paramount, as AI systems often handle sensitive student information. Institutions must ensure robust data protection measures to safeguard against breaches or misuse (Wang, 2020; Nguyen et al., 2023). Bias in AI algorithms is another concern, as it can lead to unfair treatment of certain student groups. It is essential to continuously audit and refine AI systems to ensure fairness and transparency (Holstein & Aleven, 2021). Additionally, the potential for AI to replace human educators poses ethical questions about the role of technology in education. Balancing AI’s capabilities with human oversight is crucial to maintaining ethical standards in educational practices (Wang, 2020). This aligns with the UNESCO (2021b) recommendations on the ethics of AI, which emphasize the importance of human-centered AI and the need to prioritize human values in AI development and implementation.
The future of AI tutoring in business education looks promising, with significant potential to enhance learning experiences and operational efficiency. As AI technologies continue to evolve, their integration into educational contexts will likely become more sophisticated and widespread. Institutions that embrace AI tutoring systems can expect to see improvements in student engagement, academic performance, and overall institutional effectiveness. However, it is crucial to address the ethical considerations and ensure that AI is used responsibly and transparently. By following best practices, engaging in ongoing research, and prioritizing ethical considerations, educational institutions can benefit from the full potential of AI to transform business education and prepare students for the challenges and opportunities of the 21st-century workforce.

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Dr. Ahmad Hajeer Senior Lecturer
Budapest Business University Hajeer.ahmad@uni-bge.hu

Erna Dóra Zalkodi Graduate Student
Budapest Business University erna@zalkodi.hu