Uses of AI in Business Education for Students

This chapter introduces the incorporation of artificial intelligence (AI) tools in business education for students. Its goal is to familiarize learners with the basics of AI and demonstrate its relevance in the business context. The chapter also presents various practical AI tools, offering students opportunities to enhance their understanding and problem-solving skills.

5 ways Artificial Intelligence (AI) is revolutionizing the business landscape

1. Tailored Learning Experiences: AI can customize learning for each student, adapting to their unique learning style, preferences, and performance. This ensures a more effective and personalized educational journey.
2. Realistic Business Simulations:  Generative AI could be used to create simulations that mimic real-world business interactions, such as negotiations and sales pitches. This creates a safe space for refining decision-making and problem-solving skills in a controlled environment.
3. Data Analysis: AI assists in navigating through vast datasets to unveil critical business trends and patterns. This equips business students with valuable insights that might be challenging to discover using traditional methods.
4. Intelligent Teaching Tools: AI-powered tools like chatbots, virtual assistants, and intelligent tutors provide real-time assistance and feedback. These tools optimize learning efficiency, offering support precisely when business students need it.
5. Enhanced Decision-Making: AI’s analytics and insights empower students to make informed, data-driven business decisions. This elevated decision-making capability positions them to have a more significant impact in the corporate world.

Useful AI tools

Limitations to using AI

AI tools have inherent limitations and constraints that impact their ability to think critically, analyze, and express human-like emotions. These tools lack comprehension of the meaning behind words and responses, leading to a lack of true originality, insight, or depth in their interactions. The accuracy, authenticity, and trustworthiness of outputs from AI tools are consistently in question. One reason is the lack of transparency in understanding how these tools generate responses, as well as the fact that they are trained on data from the web, which contains both factual and opinion-based information. The challenge of distinguishing between fact and opinion further complicates the reliability of AI-generated content. Biases can be present in the responses of AI tools due to the data used in their training.

Despite efforts to ensure objectivity, the potential for bias remains. OpenAI has taken steps to mitigate biases, but achieving complete neutrality is an ongoing challenge. Additionally, attempts to enhance neutrality in AI tools may raise ethical concerns related to worker exploitation. Instances of low-wage compensation for human workers involved in labeling content for AI training highlight the ethical dilemmas associated with embedding safety and morality into these systems. Questions arise about the neutrality of content labeling based on human judgments and the consideration of cultural differences in the process. This raises broader ethical considerations about the business practices employed to instill ethics and morality into algorithms and machines.

Refer to the McMaster AI policy here.

Take a look at our previous sections for a more robust description of the strengths and weaknesses of AI and the ethical considerations.