Educational Leadership and Management

Training Course on Artificial Intelligence for Student Performance Prediction

Introduction

In the era of data-driven education, Artificial Intelligence (AI) has emerged as a powerful tool to enhance learning outcomes, streamline administrative processes, and personalize student support. Training Course on Artificial Intelligence for Student Performance Prediction is designed to equip educators, administrators, and education technology specialists with in-demand skills to leverage machine learning, predictive analytics, and intelligent systems to forecast academic success and intervene effectively. With AI's capacity to process vast educational data, institutions can now identify at-risk students early, tailor instructional approaches, and elevate overall student achievement.

This hands-on, industry-relevant training blends AI theory, predictive modeling, educational data mining, and real-world applications to transform academic institutions. Participants will learn how to build and interpret AI models using student data, improve decision-making with intelligent dashboards, and ethically implement AI in education. By the end of this course, attendees will gain practical experience with tools like Python, TensorFlow, and Power BI, and be prepared to lead data-driven transformation in their organizations.

Programme Curriculum

Training Course on Artificial Intelligence for Student Performance Prediction

Introduction

Course Objectives

Understand the fundamentals of AI in education and machine learning algorithms.
Analyze historical and real-time student performance data for predictive modeling.
Build AI models for academic success prediction using Python and TensorFlow.
Apply data visualization techniques to track student progress and insights.
Explore predictive analytics to identify and support at-risk learners.
Use learning analytics dashboards for informed educational decision-making.
Integrate AI-based early warning systems in school environments.
Evaluate the ethical and legal implications of AI in student monitoring.
Optimize student retention strategies with AI recommendations.
Enhance personalized learning through intelligent tutoring systems.
Conduct data preprocessing for cleaner, more accurate model predictions.
Leverage automated grading systems to reduce educator workload.
Develop an AI implementation roadmap tailored to institutional needs.

Target Audience

School Administrators
University Lecturers
Curriculum Developers
EdTech Entrepreneurs
Policy Makers in Education
Data Scientists in Education
Learning Analytics Researchers
IT Professionals in Academic Institutions

Course Duration: 10 days

Course Modules

Module 1: Introduction to AI in Education

Definition and scope of AI in learning environments
Key components of AI systems
Overview of AI's impact on education
Evolution of student performance tracking
Benefits of predictive technologies
Case Study: AI transformation at Georgia State University

Module 2: Basics of Predictive Analytics

Fundamentals of predictive analytics
Understanding the prediction lifecycle
Types of prediction models
Regression vs. classification
Common tools and platforms
Case Study: Predicting dropout rates using logistic regression

Module 3: Data Collection and Preprocessing

Identifying relevant student data sources
Cleaning and preparing datasets
Handling missing or inconsistent data
Data transformation techniques
Feature engineering basics
Case Study: Data preprocessing pipeline in K-12 school system

Module 4: Machine Learning Models for Prediction

Supervised vs. unsupervised learning
Building decision trees and neural networks
Evaluating model accuracy
Hyperparameter tuning
Real-world datasets in education
Case Study: Neural network predicting math scores

Module 5: Tools and Technologies for AI Development

Introduction to Python and Jupyter Notebook
Using TensorFlow and Scikit-learn
Integrating Power BI for visualization
Model deployment basics
Collaborative coding tools (Git, Colab)
Case Study: End-to-end AI project in a community college

Module 6: Student Performance Indicators

Attendance, participation, and GPA
Online learning behavior and engagement
Socioeconomic factors and performance
Learning disabilities and academic outcomes
Real-time performance tracking
Case Study: AI-enhanced LMS identifying struggling students

Module 7: Early Warning Systems

Designing threshold models
Alert mechanisms and notifications
Data-driven intervention strategies
Measuring effectiveness of warnings
Customizing for diverse institutions
Case Study: Real-time alerts in South African schools

Module 8: Personalization with AI

Student profiling and adaptive learning
Intelligent tutoring systems (ITS)
AI in learning management systems
Content recommendation engines
Monitoring learning preferences
Case Study: Personalized e-learning at Arizona State University

Module 9: Visualization of Predictive Outcomes

Interactive dashboards
Key performance indicators (KPIs)
Heatmaps and score charts
Tracking academic trends
Communicating predictions effectively
Case Study: Power BI dashboard for university retention

Module 10: AI-Driven Retention Strategies

Predicting student attrition
Proactive academic advising
Resource allocation for support
Evaluating retention model ROI
Communication frameworks
Case Study: AI-led retention success at Ivy Tech

Module 11: Ethical AI Use in Education

Bias in data and algorithms
Student privacy and FERPA compliance
Transparency and accountability
Responsible AI frameworks
Building trust with stakeholders
Case Study: Legal implications of AI in New York schools

Module 12: Integration with LMS Platforms

AI plug-ins for Moodle, Canvas, Blackboard
API connections and automation
Real-time analytics in LMS
Instructor and student dashboards
LMS as a central data hub
Case Study: Moodle AI integration at University of Nairobi

Module 13: Grading Automation Systems

AI-based test scoring
NLP for essay grading
Feedback generation models
Time-saving benefits
Addressing grading fairness
Case Study: AI-automated grading in South Korea

Module 14: Monitoring and Continuous Improvement

Post-deployment monitoring
Model retraining and updates
Feedback loops from users
Key performance review
Scalability in larger systems
Case Study: Continuous model improvement at MITx

Module 15: Building an AI Strategy for Schools

Setting AI implementation goals
Institutional AI readiness assessment
Budgeting and funding sources
Stakeholder engagement and training
Measuring impact and scaling
Case Study: Developing an AI roadmap for NYC schools

Training Methodology

Interactive instructor-led sessions
Hands-on labs using real student datasets
Collaborative group projects
Step-by-step coding walkthroughs
Quizzes and mini assessments
Capstone project with performance prediction dashboard

Register as a group from 3 participants for a Discount

Send us an email: info@fineskilltrainingcenter.org or call +254769199797

Certification

Upon successful completion of this training, participants will be issued with a globally- recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

a. The participant must be conversant with English.

b. Upon completion of training the participant will be issued with an Authorized Training Certificate

c. Course duration is flexible and the contents can be modified to fit any number of days.

d. The course fee includes facilitation training materials, 2 coffee breaks, buffet lunch and A Certificate upon successful completion of Training.

e. One-year post-training support Consultation and Coaching provided after the course.

f. Payment should be done at least a week before commence of the training, to Fineskill Training Center account, as indicated in the invoice so as to enable us prepare better for you.

Skills & Software Covered

ML frameworks predictive analytics student data modeling

Programme Fee

Duration: 10 Days

Method: Online or Physical

Online Price: $2,000

Physical Price: $3,000

Certification: Accredited Certificate

View Available Dates 📄 Download Course Outline (PDF)

Available Sessions

Location

Month

Year

Oct 19 2026

19 Oct — 30 Oct 2026

💻 Online / Virtual session

Online $2,000