Artificial intelligence (AI) is the broad field of computer science concerned with creating intelligent agents, which are systems that can reason, learn, and act autonomously from data. It encompasses a range of technologies, including machine learning, in which computers use algorithms to train themselves to identify patterns or make predictions from a dataset. Machine learning models can learn and adapt through experience and natural language processing, enabling them to understand and respond to human language.
AI is already used in a range of applications across sectors and will continue to influence the higher education solution space, including smart content recommender systems, personalized student support, adaptive degree planning, and more. It has the potential to enrich the student experience, personalize and deepen learning, improve student support, and ultimately increase student outcomes. However, as solutions are developed, there is a need to focus on not only trustworthy AI but also equitable AI.
The AI development process encompasses a series of stages involved in designing, building, and deploying artificial intelligence (AI) and machine learning (ML) systems. While specific methodologies and frameworks may vary depending on the context and requirements of a particular project, the AI development process typically involves the following stages in the graphic below:
Stakeholder engagement, transparency, and bias identification and mitigation live throughout the machine learning lifecycle and support the development of equitable AI systems. By integrating these practices into every stage of the machine learning lifecycle, developers can create AI systems that are not only technically robust and efficient but also ethical, accountable, and equitable. This holistic approach to AI development promotes trust, fairness, and inclusivity in AI technologies.
Check out our article on AI use cases in higher education.
Explore an in-depth overview of AI/ML methodologies by visiting the "Types of Machine Learning" section of this guide.
Artificial intelligence (AI) is the broad field of computer science concerned with creating intelligent agents, which are systems that can reason, learn, and act autonomously from data. It encompasses a range of technologies, including machine learning, in which computers use algorithms to train themselves to identify patterns or make predictions from a dataset. Machine learning models can learn and adapt through experience and natural language processing, enabling them to understand and respond to human language.
AI is already used in a range of applications across sectors and will continue to influence the higher education solution space, including smart content recommender systems, personalized student support, adaptive degree planning, and more. It has the potential to enrich the student experience, personalize and deepen learning, improve student support, and ultimately increase student outcomes. However, as solutions are developed, there is a need to focus on not only trustworthy AI but also equitable AI.
The AI development process encompasses a series of stages involved in designing, building, and deploying artificial intelligence (AI) and machine learning (ML) systems. While specific methodologies and frameworks may vary depending on the context and requirements of a particular project, the AI development process typically involves the following stages in the graphic below:
Stakeholder engagement, transparency, and bias identification and mitigation live throughout the machine learning lifecycle and support the development of equitable AI systems. By integrating these practices into every stage of the machine learning lifecycle, developers can create AI systems that are not only technically robust and efficient but also ethical, accountable, and equitable. This holistic approach to AI development promotes trust, fairness, and inclusivity in AI technologies.
Check out our article on AI use cases in higher education.
Explore an in-depth overview of AI/ML methodologies by visiting the "Types of Machine Learning" section of this guide.
