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- About
Artificial Intelligence and Learning Analytics in Education Certificate
This Artificial Intelligence and Learning Analytics in Education certificate program is a 12-credit hour fully online asynchronous post-baccalaureate certificate. Artificial intelligence (AI) is reshaping PK–12 education by influencing how students learn, how teachers instruct, and how schools use data to guide decisions. However, these tools can also raise concerns about privacy, bias, transparency, and ethics. Additionally, many educators lack preparation to interpret and apply AI technology and data effectively. Building educator capacity in these areas is essential for responsible and effective implementation. AI and learning analytics (LA) can personalize instruction, identify learning gaps, support targeted interventions, and automate routine tasks. The certificate program will focus on the responsible integration of AI and LA in educational settings. The certificate features a cohort-based model that provides a foundation in AI ethics and applications in learning environments, along with foundational and advanced competencies in learning analytics.
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Target Audience
The program is designed to serve three audiences: certificate-seeking professionals who already hold a bachelor’s degree in education (or a closely related field), Clemson graduate students across disciplines seeking expertise in AI and learning analytics in educational contexts, and MEd Learning Sciences students who may complete the four-course sequence as part of their program coursework.
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Program Objectives
Upon completion of the certificate in Artificial Intelligence and Learning Analytics in Education, students will be able to:
- Demonstrate a comprehensive understanding of the key concepts, principles, and ethical issues surrounding the use of AI and LA in educational contexts;
- Apply analytical and AI-driven tools to collect, interpret, and visualize educational data to inform teaching and learning practices;
- Critically evaluate data-driven decision-making processes and assess the reliability, validity, and fairness of AI systems used in education;
- Design and implement ethical and inclusive AI-enhanced strategies that support personalized learning and improve student outcomes; and
- Integrate reflective, evidence-based, and research-informed approaches into their professional practice, culminating in a practical project that demonstrates the effective application of AI and learning analytics in real educational settings.
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Program Requirements
Students must take the following 4 courses for a total of 12 credit hours:
EDF 8050 - Foundations of Artificial Intelligence in Learning Environments (3 credit hours)
EDF 8060 - Introduction to Learning Analytics (3 credit hours)
EDF 8150 - Artificial Intelligence and Ethics in Learning Environments (3 credit hours)
EDF 8160 - Advanced Learning Analytics (3 credit hours)
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Required Course Descriptions
EDF 8050 - Foundations of Artificial Intelligence in Learning Environments (3 credit hours)
Description: Focuses on foundational concepts of Artificial Intelligence (AI) in learning environments from historical and modern perspectives. Key topics include how AI works, applications in education and everyday life, AI theoretical and literacy frameworks, and opportunities and ethics related to prompt engineering, generative AI, and large language models. Prerequisites: None
EDF 8060 - Introduction to Learning Analytics (3 credit hours)
Description: Fundamental techniques for transforming educational data into actionable insights about learning processes. Key topics include the foundations of learning analytics, basics of statistical programming, processes in data preparation, application of basic statistics to educational data, creation of effective visualizations of educational data, and use of machine learning methods such as predictive modeling of student outcomes and cluster analysis of educational data. Prerequisites: None
EDF 8150 - Artificial Intelligence and Ethics in Learning Environments (3 credit hours)
Description: Examines the growing role of artificial intelligence across learning environments and prepares educators and learning scientists to critically evaluate its ethical dimensions. Participants will explore issues of equity, privacy, and learner agency to make informed, human-centered decisions. The course empowers graduates to design inclusive and transparent AI-integrated educational experiences aligned with core pedagogical values. Prerequisites: None
EDF 8160 - Advanced Learning Analytics (3 credit hours)
Description: Advanced learning analytics methods for uncovering deep insights into learning processes and building state-of-the-art educational tools, such as modeling longitudinal processes in education, network analysis of complex social and knowledge structures in educational settings, predictive and evaluative uses of artificial intelligence in education, and the use of large language models to develop explainable feedback systems in education. Prerequisites: EDF 8060
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Admission Requirements
- Must have a bachelor’s degree in Education (or a closely related field)
- Complete the Graduate School Application
- Current Resume/CV
The four-course, asynchronous Artificial Intelligence and Learning Analytics in Education certificate program admits students for a Spring entrance term. The deadline to apply is December 1.
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Find Out More
For more information regarding the certificate or questions associated with applying to the certificate, please contact Madison Hudson at mhedden@clemson.edu.
