Training Course on Leading the Integration of AI-Powered Tutors and Assistants
Training Course on Leading the Integration of AI-Powered Tutors and Assistants equips educators, administrators, and edtech leaders with the strategic tools and knowledge required to successfully implement AI-driven learning environments.
Course Overview
Training Course on Leading the Integration of AI-Powered Tutors and Assistants
Introduction
As artificial intelligence (AI) continues to redefine the landscape of education, institutions are turning to AI-powered tutors and digital assistants to personalize learning, boost student engagement, and optimize instructional delivery. Training Course on Leading the Integration of AI-Powered Tutors and Assistants equips educators, administrators, and edtech leaders with the strategic tools and knowledge required to successfully implement AI-driven learning environments. Through real-world case studies, hands-on modules, and practical implementation strategies, this training empowers participants to transform learning through cutting-edge AI tools.
By completing this course, leaders will develop future-ready competencies to design, lead, and evaluate AI-integrated education systems. Participants will gain expertise in ethical AI use, data-driven decision-making, digital equity, adaptive learning, and stakeholder engagement. The course is designed to prepare professionals for the next generation of teaching and learning innovation.
Course Objectives
- Understand the fundamentals of AI-powered education tools
- Analyze case studies of successful AI tutor implementations
- Lead the strategic integration of digital AI assistants in classrooms
- Promote data privacy and ethical use of educational AI
- Design inclusive, AI-supported personalized learning pathways
- Build educator capacity for AI-assisted instruction
- Develop digital literacy and critical thinking in AI-enhanced environments
- Optimize engagement through adaptive learning technologies
- Leverage AI for differentiated and competency-based learning
- Ensure equity and accessibility in AI-driven platforms
- Utilize AI analytics to inform instruction and assessment
- Drive institutional change for AI adoption in education
- Foster innovation through AI-augmented collaborative learning
Target Audiences
- School Principals and Administrators
- University Faculty and Deans
- EdTech Entrepreneurs
- Instructional Designers
- K–12 and Higher Ed Teachers
- Policy Makers in Education
- AI/ML Developers in EdTech
- Learning and Development Professionals
Course Duration: 5 days
Course Modules
Module 1: Introduction to AI in Education
- Understand AI, machine learning, and NLP in learning
- History and evolution of AI tutors and digital assistants
- Key terminologies and tools
- Scope and limitations of AI in education
- Global trends in AI integration in schools
- Case Study: China's Squirrel AI in personalized learning
Module 2: Designing an AI Strategy for Your Institution
- Align AI tools with institutional goals
- Define objectives and success indicators
- Risk management and governance
- Budgeting and resource allocation
- Forming cross-functional AI integration teams
- Case Study: Arizona State University's AI roadmap
Module 3: Personalized and Adaptive Learning
- Differentiating instruction using AI
- Learning styles and personalized pathways
- AI-based assessment models
- Real-time feedback and smart recommendations
- Challenges in adaptive learning design
- Case Study: Carnegie Learning's MATHia platform
Module 4: Ethical, Legal & Data Privacy in AI
- Data governance and student privacy policies
- GDPR, FERPA, and local compliance issues
- AI bias and algorithm transparency
- Promoting digital ethics in AI instruction
- Informed consent and AI usage policies
- Case Study: UK’s Ofqual exam algorithm controversy
Module 5: Building Educator Capacity
- Professional development for AI integration
- Overcoming resistance to AI adoption
- Creating a culture of innovation
- Leveraging AI to reduce administrative load
- Teacher-led design of AI tools
- Case Study: Microsoft’s AI for Educators initiative
Module 6: Enhancing Student Engagement
- Interactive conversational agents in learning
- AI to support special education and diverse learners
- Real-time feedback and gamification
- AI-driven goal tracking and rewards
- Strategies to maintain human connection
- Case Study: Jill Watson—AI teaching assistant at Georgia Tech
Module 7: Using AI for Instructional Analytics
- AI and predictive analytics in education
- Identifying at-risk learners with early signals
- Visualizing student performance patterns
- Using dashboards and learning record stores
- Ethical issues in student surveillance
- Case Study: Civitas Learning in higher education outcomes
Module 8: Sustaining AI Innovation and Scaling
- Piloting vs full-scale implementation
- Continuous evaluation and feedback loops
- Building AI partnerships and vendor relationships
- Change leadership in AI transitions
- Community and stakeholder engagement
- Case Study: Singapore's Smart Nation Schools
Training Methodology
- Blended learning: Interactive online sessions and live workshops
- Hands-on activities: Guided AI tool simulations and walkthroughs
- Peer collaboration: Breakout sessions and group projects
- Mentoring: Personalized coaching by AI education experts
- Capstone project: Institutional AI integration plan development
Register as a group from 3 participants for a Discount
Send us an email: info@datastatresearch.org or call +254724527104
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 DATASTAT CONSULTANCY LD account, as indicated in the invoice so as to enable us prepare better for you.