Professional Certificate in AI-enhanced Formative Assessment
-- viewing nowArtificial Intelligence (AI) is revolutionizing the way we assess student learning. AI-enhanced Formative Assessment is designed for educators and instructional designers who want to leverage AI to improve student outcomes.
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About this course
Unlock the full potential of AI in assessment with this Professional Certificate program. Learn how to harness AI-powered tools to personalize learning, identify knowledge gaps, and inform instruction.
Develop skills in AI-driven assessment design, data analysis, and implementation strategies.
Enhance your teaching practice and stay ahead of the curve in education technology.
Explore the possibilities of AI-enhanced Formative Assessment today and take the first step towards transforming your teaching practice.
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Course details
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Unit 1: Introduction to AI-enhanced Formative Assessment - This unit provides an overview of the concept of formative assessment, the role of AI in enhancing assessment practices, and the benefits of AI-enhanced formative assessment in improving student outcomes. •
Unit 2: Machine Learning for Assessment - This unit introduces the basics of machine learning, including supervised and unsupervised learning, and how these concepts can be applied to assessment practices to improve accuracy and efficiency. •
Unit 3: Natural Language Processing for Text Analysis - This unit explores the application of natural language processing (NLP) techniques to analyze student performance data, including sentiment analysis and text classification. •
Unit 4: AI-driven Adaptive Assessments - This unit delves into the use of AI to create adaptive assessments that adjust to individual students' needs, abilities, and learning styles. •
Unit 5: Data Analytics for Assessment - This unit focuses on the use of data analytics to gain insights into student performance, identify areas of improvement, and inform instruction. •
Unit 6: Ethics and Bias in AI-enhanced Assessment - This unit examines the ethical considerations surrounding the use of AI in assessment practices, including bias, fairness, and transparency. •
Unit 7: AI-enhanced Feedback and Feedback Loops - This unit explores the use of AI to provide personalized feedback to students, and the importance of feedback loops in improving student learning outcomes. •
Unit 8: AI-driven Personalized Learning - This unit introduces the concept of personalized learning and how AI can be used to create tailored learning experiences for individual students. •
Unit 9: AI-enhanced Teacher Professional Development - This unit focuses on the need for teacher professional development in the context of AI-enhanced formative assessment, including strategies for teachers to stay up-to-date with the latest developments. •
Unit 10: Implementing AI-enhanced Formative Assessment in Practice - This unit provides guidance on implementing AI-enhanced formative assessment in real-world settings, including practical strategies for teachers and administrators.
Unit 1: Introduction to AI-enhanced Formative Assessment - This unit provides an overview of the concept of formative assessment, the role of AI in enhancing assessment practices, and the benefits of AI-enhanced formative assessment in improving student outcomes. •
Unit 2: Machine Learning for Assessment - This unit introduces the basics of machine learning, including supervised and unsupervised learning, and how these concepts can be applied to assessment practices to improve accuracy and efficiency. •
Unit 3: Natural Language Processing for Text Analysis - This unit explores the application of natural language processing (NLP) techniques to analyze student performance data, including sentiment analysis and text classification. •
Unit 4: AI-driven Adaptive Assessments - This unit delves into the use of AI to create adaptive assessments that adjust to individual students' needs, abilities, and learning styles. •
Unit 5: Data Analytics for Assessment - This unit focuses on the use of data analytics to gain insights into student performance, identify areas of improvement, and inform instruction. •
Unit 6: Ethics and Bias in AI-enhanced Assessment - This unit examines the ethical considerations surrounding the use of AI in assessment practices, including bias, fairness, and transparency. •
Unit 7: AI-enhanced Feedback and Feedback Loops - This unit explores the use of AI to provide personalized feedback to students, and the importance of feedback loops in improving student learning outcomes. •
Unit 8: AI-driven Personalized Learning - This unit introduces the concept of personalized learning and how AI can be used to create tailored learning experiences for individual students. •
Unit 9: AI-enhanced Teacher Professional Development - This unit focuses on the need for teacher professional development in the context of AI-enhanced formative assessment, including strategies for teachers to stay up-to-date with the latest developments. •
Unit 10: Implementing AI-enhanced Formative Assessment in Practice - This unit provides guidance on implementing AI-enhanced formative assessment in real-world settings, including practical strategies for teachers and administrators.
Career path
| **Career Role** | Job Description |
|---|---|
| Artificial Intelligence/Machine Learning Engineer | Design and develop intelligent systems that can learn and adapt to new data, with a focus on natural language processing, computer vision, and predictive analytics. |
| Data Scientist | Extract insights and knowledge from data using statistical models, machine learning algorithms, and data visualization techniques to inform business decisions. |
| Business Intelligence Developer | Design and implement data visualization tools and business intelligence solutions to help organizations make data-driven decisions and improve operational efficiency. |
| Quantitative Analyst | Apply mathematical and statistical techniques to analyze and model complex systems, with a focus on risk management, portfolio optimization, and financial forecasting. |
| Computer Vision Engineer | Develop algorithms and models that enable computers to interpret and understand visual data from images and videos, with applications in self-driving cars, surveillance, and healthcare. |
Entry requirements
- Basic understanding of the subject matter
- Proficiency in English language
- Computer and internet access
- Basic computer skills
- Dedication to complete the course
No prior formal qualifications required. Course designed for accessibility.
Course status
This course provides practical knowledge and skills for professional development. It is:
- Not accredited by a recognized body
- Not regulated by an authorized institution
- Complementary to formal qualifications
You'll receive a certificate of completion upon successfully finishing the course.
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PROFESSIONAL CERTIFICATE IN AI-ENHANCED FORMATIVE ASSESSMENT
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who has completed a programme at
London School of Planning and Management (LSPM)
Awarded on
05 May 2025
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