Throughout our three-part blog series, we have discussed some of the foundational work on Artificial Intelligence (AI) in education, as well as current uses and concerns. In our final blog about AI in the classroom, we want to focus on the future. What impact might these tools have in the future, and how should institutions think about using them?

In our first blog, "Teaching in the AI-Powered Future," we discussed how AI is integrated into the classroom in various ways, such as personalized learning and virtual teaching assistants. The blog emphasizes that while AI can be helpful in certain aspects of education, it should not replace human teachers. Instead, AI should be used to support teachers and provide them with more time to focus on individualized instruction and building relationships with their students.

Our second blog, "The Impact of AI in Our Classrooms," discussed the potential benefits of AI in education, such as improving student outcomes. But we also discussed concerns around the ethical use of AI in education, such as privacy and bias issues. The blog emphasized the importance of implementing AI responsibly and ethically, focusing on improving student learning and outcomes.

Both blogs acknowledged the potential benefits of AI in education but caution against relying too heavily on AI and highlight the importance of responsible implementation.

We touched on bias in both of our previous blogs, but in this third one, we want to focus more on ways to reduce bias in AI and think about what AI will look like in the future.

What Lies Ahead

AI has come a long way in recent years, and there are still many exciting developments on the horizon. As AI is increasingly used in high-stakes applications like healthcare and finance, there is a growing need for models that are easily understood and trusted by users. As more and more devices connect to the internet, AI will play a key role in processing and making sense of the data generated by these devices. With the rise of chatbots and virtual assistants, there is a growing need for AI systems that can understand and respond to human language in a natural way. As AI becomes more ubiquitous, there is an increasing need to address ethical considerations such as bias, transparency, and accountability.

AI can revolutionize how courses are designed and delivered in higher education by providing real-time analysis and feedback on student learning. By leveraging machine learning algorithms and data analytics, AI can help educators identify areas where students are struggling and adjust the course content to meet their needs.

AI can be used for real-time course design by analyzing student data such as engagement levels, progress, and performance on assessments. By analyzing data from multiple students, AI can identify common areas of difficulty and help educators make informed decisions about curriculum development. For example, if a concept is consistently difficult for students, educators can adjust the course content to provide additional support and resources.

Real-time course design with AI can also assist educators in adapting to changing circumstances and unexpected events. A prime example of this was during the COVID-19 pandemic, when courses had to be quickly adapted for online delivery. AI can help educators analyze data on student engagement and performance in these new contexts and make necessary adjustments to the course design to ensure students are still receiving a high-quality learning experience.

Overall, real-time course design with AI can improve higher education's effectiveness and efficiency. By providing personalized learning paths, identifying areas of difficulty, and adapting to changing circumstances, AI can help educators create engaging and effective learning experiences that meet the needs of all students.

Faculty play a critical role in developing and delivering curriculum, providing mentorship and guidance to students, and conducting research and scholarship. These are complex tasks that require a deep understanding of the subject matter, as well as the ability to engage and motivate students.

AI is not capable of the kind of interpersonal interactions that are essential to teaching and learning. Human instructors can provide emotional support, encouragement, and inspiration to students, which are crucial elements of the learning process. They are also able to adapt their teaching strategies to the individual needs of each student, which is difficult for an AI-powered system to replicate.

Faculty will continue to play a critical role in developing and delivering curriculum, providing mentorship and guidance to students, and conducting research and scholarship.

Ethical Considerations

The use of AI in education has the potential to improve learning outcomes and provide personalized learning experiences for students. However, the use of AI also raises ethical considerations that must be addressed to ensure it is used in a responsible and beneficial way. For many of these considerations, educational institutions will rely on their education technology partners. Institutions should carefully review the practices of their education technology partners to understand how these considerations are addressed.

AI systems collect a significant amount of personal information on students, including their learning habits, progress, and behavior. In addition to ensuring that appropriate data privacy measures are in place, educational institutions must ensure that this data is collected and used responsibly and transparently. To further ensure student privacy, clear guidelines on the type of data that is collected and how it will be used should be codified and shared with learners.

Educational institutions must ensure that the algorithms and decision-making processes used by AI systems are transparent and understandable. Students and teachers should be able to understand how decisions are made and be able to challenge them if necessary.

Educational institutions must be accountable for the decisions made by AI systems. This includes ensuring decisions are based on accurate and relevant data and that all results are valid and reliable.

AI systems should not replace human teachers or decision-makers. Human oversight is necessary to ensure that AI systems are used appropriately and equitably and that decisions are made in the best interests of students.

AI systems should be accessible to all students, regardless of their socioeconomic background or disability status. Educational institutions must ensure that AI systems do not perpetuate existing inequalities in education.

Educational institutions must ensure that AI systems are safe and do not pose a risk to students' physical or emotional well-being. Addressing these ethical considerations is essential to ensure that the use of AI in education is responsible, fair, and beneficial to students. Educational institutions must prioritize ethical considerations when designing and implementing AI systems to ensure they are used in a way that promotes positive learning outcomes for all students.

Reducing Bias

To ensure that AI systems treat people fairly and avoid perpetuating existing inequalities, reducing bias is crucial. Here are some ways to achieve this goal:

1. Ensure diversity in training data: AI systems learn from the data they are trained on, and if it only represents a specific group of people or biases, the AI system may make biased decisions. Thus, it is crucial to ensure that the training data is diverse and representative of the entire population.
2. Address bias in algorithms: Bias can be introduced at various stages of the AI development process. Hence, it is vital to audit the algorithms and identify any potential sources of bias. Techniques such as adversarial testing can help identify and address biases in the algorithms.
3. Implement fairness metrics: Developers can use fairness metrics to measure an AI system's fairness, identify potential sources of bias, and provide insight into how to improve the system's fairness.
4. Involve diverse stakeholders: It is important to include diverse stakeholders in the AI development process, including individuals from different racial, ethnic, and socio-economic backgrounds, to ensure that the AI system is fair and unbiased.
5. Monitor the AI system's performance: It is crucial to continuously evaluate an AI system's performance for fairness and bias after deploying it. If any bias is detected, it should be addressed immediately to prevent harm to individuals or groups.

Reducing bias in AI systems requires a combination of technical and social approaches. It is crucial to prioritize fairness and inclusivity throughout the development process and continuously monitor the AI system's performance to ensure that it remains fair and unbiased.