AI in the Classroom: Shaping the Future of Business Education

As artificial intelligence (AI) continues to transform industries, business schools and higher education more broadly must adapt to the rapid pace of innovation to ensure that today’s students and tomorrow’s leaders are prepared to thrive in an increasingly technology-driven world. This requires a broad-based approach to education, ensuring that AI is accessible not just to those with technical aptitude but to students approaching business from all backgrounds. 

AI in Business Teaching

The COVID-19 pandemic forced us to revisit our approach to pedagogy, revealing that what worked in person didn’t always translate to a virtual environment. Similarly, AI might feel like another disruption, but this time, we have a choice in how we adopt it. One option is to bristle at the notion that we need to adapt our teaching. Some may resist incorporating AI into the classroom – restricting or banning its use. But, for business education, this is a short-term solution that will create long-term challenges. Just as we don’t prohibit using calculators and spellcheck, we can’t imagine prohibiting students from leveraging the tools at their disposal. This not only limits their potential but also leaves them unprepared for the realities of the modern workplace. Today, these tools include AI.

As AI tools become as ubiquitous as Google Search or mobile devices, incorporating AI into teaching becomes essential. Business leaders need to be prepared and aware of both the opportunities and challenges that AI presents. This implies that we need to not only allow the use of AI tools but also ensure that students understand these tools to make informed and effective decisions. Given the diversity of backgrounds among business students, schools need to offer learning opportunities that cater to four degrees of fluency on which to build student knowledge:

1. Foundational Knowledge of AI: All students require a solid grounding in the fundamentals of AI. What is it? How does it work? At this level of fluency, we ensure that all students are informed about AI and can meaningfully engage with common tools. Providing foundational knowledge does not imply offering courses that teach tools such as prompt engineering, but it emphasizes utilizing AI to augment learning experiences. The learning could be offered by foundation courses in accounting, finance, marketing, strategy or operations, but the learning could be amplified through engagement with AI tools. Thus, students could benefit from AI-enabled tools that serve as virtual teaching assistants, offering guidance on course content, feedback on homework or insights on tangential content. The exposure to AI as a learning tool will ensure that future business leaders understand how AI can help them make better decisions, analyze data or overcome mental blocks. These foundation courses are essential to preparing informed and AI-powered leaders of tomorrow.
2. Business Applications of AI: Building on the foundational knowledge, the next level of AI fluency focuses on its real-world business applications. From automating repetitive tasks to uncovering new market opportunities, students must understand how AI can be used as a tool to solve business problems. This goes beyond the use of AI for common tasks in any discipline, but considering opportunities enabled by AI for generating content, augmenting decisions, automating processes or personalizing information [1]. Understanding these applications will help students craft AI-powered strategies, enabling businesses to compete more effectively. Programs offering courses such as “AI for Marketing,” “AI for Operations” or “AI for Finance” should be integrated at all levels – undergraduate, graduate and executive education – to foster AI fluency.
3. Societal and Ethical Implications of AI: Once students have an understanding of how organizations are leveraging AI, the next step is to view it within a broader context – what is AI’s impact on society? Just because we have access to a new set of tools does not mean we should use them in every situation because these tools may have inherent limitations. The capabilities of AI come with responsibilities, and students must understand the ethical implications of using these tools. At this level of fluency, teaching content on algorithmic bias, data privacy and the liar’s dividend is essential for ensuring that future business leaders are equipped not just with AI skills but with the moral compass to responsibly use AI. Thus, with these topics, we will prepare a well-rounded workforce that understands not only the potential of AI but also the best practices of adopting and utilizing the tools for a positive impact on our society. This understanding is critical for fostering sustainable AI strategies that both benefit businesses and protect the broader society and stakeholders.
4. Technical Application Development with AI: For students who stay inclined, the final level of fluency aims to help them build deeper technical skills through hands-on courses that culminate with developing and implementing AI solutions. Such training will provide students with the necessary technical skills and preparation to deploy these skills in their organizations. Courses catering to this fluency would provide the technical know-how for reducing AI “hallucinations,” creating AI agents, developing data governance and deploying AI solutions within organizations. By learning to work alongside both technical and business teams, students will be prepared to lead AI innovations within their industries. This technical knowledge is essential for those who will guide businesses into the future by staying ahead of the AI curve.

As we strive to develop AI fluency among students, we need to build AI tools that support such efforts. One way in which AI is already being deployed is through the use of chatbot-based systems to aid students in reviewing course material. Basic questions no longer require a visit to a TA during office hours. Instead, students can get quick responses when they need them. Building on such Q&A systems, automated feedback is another AI solution that can facilitate learning. This need not be restricted to automatically grading final submissions. Rather, feedback can be provided while students are grappling with problems, guiding them along the way and helping them refine their arguments and solutions. AI has to be in every aspect of a student’s learning journey in higher education.

AI in Business Research

Outside the classroom, AI technology is also impacting business research. New opportunities bring new questions that require urgent attention, and academics are racing to provide insights at a pace that matches the rapid development of the field. In marketing, for example, researchers are investigating the returns from hyperpersonalization of advertisements and content engineering of product descriptions and website content [2]. In ethics and technology, research is exploring the uses and limitations of synthetic data and development of guardrails for developing safe AI systems. In finance, researchers are assessing the effects of algorithms for credit scoring, which may increase access to capital for some while harming it for others. Similarly, pricing algorithms can help firms make efficient decisions but can also lead to tacit collusion. all fields of business research, an urgent question is which occupations and skills will be rewarded by this new technology and which won’t.

New technology impacts the research process as much as the research content itself. In every area of knowledge, researchers armed with large language models (LLMs) now have the ability to extract and quantify semantic meaning that has previously been locked away in mountains of text. Business research, in particular, will experience an explosion of new insights because of the tremendous amount of meaning stored away in contracts, legal filings and conversation transcripts. Machine learning algorithms are increasingly used to perform forensic market research, such as detecting anomalous trading patterns, which was previously difficult to do at scale. Even at the mundane level of writing and editing, AI tools can help researchers more effectively communicate their ideas, even across barriers of culture and language.

These developments also present significant challenges to the traditional peer-review process. Reproducibility, which should be a hallmark of the scientific method, is limited by the current crop of available LLMs. AI tools can generate low-quality papers faster than journals can reject them, which may eventually overwhelm the peer-review process, as some areas of the internet are being overtaken by AI-generated slop. Most disturbingly, AI tools could help unethical researchers generate fake but plausible results that would evade our current ability to detect them. However, by the same token, AI tools excel at rooting out the most common types of misconduct, as seen in many recent examples from the medical field, and can review the growing literature to spot inconsistencies faster than any human could.

Like every industry, academic research will face its promises and challenges from new AI technology, but in the long run, a brighter future awaits when these tools are an established and adopted part of the research process. Science has always been self-correcting, but the process can be slow and painful. As we gain better understanding of the capabilities of AI, we have the opportunity to change the time scale of this process – in ways that can potentially change lives.

AI in Administration

AI is shifting not only how we teach and conduct research in business schools but also how institutions of higher education operate. Tasks such as student counseling, scheduling and summarizing meetings can be automated, freeing up time and improving efficiency of the administrative workforce.

This brings up a common concern about AI: fear of job loss. There is widespread anxiety that AI will displace human workers, particularly in administrative roles. But this inherently frames AI as a replacement for human capabilities. Instead, we need to view AI as augmenting human labor, allowing employees to focus on those aspects of their jobs in which they add the most value and derive the most meaning. Rather than looking at it as increasing the potential to eliminate jobs, we need to see the deployment of AI as supporting our teams to work smarter in fulfilling the school’s mission. This shift in administrative roles reflects a broader trend in the business world. As AI becomes more integrated into business operations, organizations will need to reimagine how they structure their workforce. By leveraging AI to work smarter, institutions can enhance productivity and employee satisfaction.

As AI continues to influence business, education, research and administration, it is critical that we prepare future leaders to navigate this rapidly evolving landscape. Business schools must prepare for AI, not as a threat but as an opportunity to enhance education, research and operational efficiency. By fostering a comprehensive understanding of AI’s capabilities, applications and ethical implications, we can equip students to lead in an AI-driven world. Ultimately, the future of business education lies in creating AI-fluent leaders who can balance technological innovation with human-centered decision-making, ensuring that AI is used to increase productivity as well as improve society as a whole. At Goizueta Business School, we are preparing our students to lead this change, helping them understand how to use AI to drive productivity while maintaining a human-centered approach to business.

References

1. Rajiv Garg, 2022, “AI-Enabled Future of Work,” OR/MS Today, November 29, https://doi.org/10.1287/orms.2022.06.03.
2. Martin Reisenbichler, Thomas Reutterer, David A. Schweidel and Daniel Dan, 2022, “Frontiers: Supporting Content Marketing with Natural Language Generation,” Marketing Science, Vol. 41, No. 3, pp. 441-452.