AI and Quality Management in Education

A Comprehensive Overview
By Silvio Nocilla

Introduction

Way back in 2013, as part of my Master’s studies, I conducted an analysis on the process of Quality Assurance (QA) in e-learning. At the time, digital learning was still finding its place within traditional education systems, and ensuring consistent standards across online platforms presented new challenges.

In 2018, I revisited and updated that initial work into a short article, reflecting how e-learning practices and quality frameworks had evolved to match the technologies and pedagogical approaches of that period.

Today, as we stand at the forefront of a new educational transformation driven by Artificial Intelligence (AI), it becomes essential once again to re-examine and adapt QA principles to meet the demands of this era. The emergence of AI in education has introduced intelligent systems capable of personalising learning, automating assessments, and generating data-driven insight offering immense potential, yet also requiring renewed frameworks of trust, transparency, and ethical responsibility.

This updated document therefore extends the principles of Quality Management in Education into the AI era, exploring how traditional QA foundations can evolve to ensure that AI-enhanced learning remains reliable, equitable, and aligned with human educational values.

Origins of Quality and AI Integration

From Industrial Quality to Intelligent Systems

Quality management originated in manufacturing, focusing on standards, precision, and continuous improvement.
In today’s educational landscape, Artificial Intelligence (AI) extends these principles by automating assessment, enhancing personalization, and supporting data-driven decision-making.

AI as a Quality Enabler

Where traditional quality management emphasized conformance and prevention of errors, AI introduces predictive analytics, learning analytics, and adaptive feedback systems to maintain and improve educational quality dynamically.

 AI-Driven Quality Standards

From ISO 9000 to AI Ethics and Transparency

While the ISO 9000 family (ISO 9001, 9004) remains foundational for quality assurance, education now requires AI-specific frameworks:

• ISO/IEC 42001 (2025) – AI Management System Standard
• UNESCO’s AI in Education Guidelines (2023) – promote ethics, transparency, and accountability
• EU AI Act (2024) – defines risk-based compliance for educational AI systems

These frameworks align with the classic QA focus: efficiency, effectiveness, and fairness—but now within intelligent systems that impact learning. 

Quality Assurance (QA) in AI-Enhanced Education

Redefining QA

Quality Assurance in education must now include the ethical, technical, and pedagogical dimensions of AI.
AI tools are increasingly used in:

• Adaptive learning platforms
• Automated grading systems
• Intelligent tutoring systems
• Chatbots and virtual assistants

Each system must be evaluated for reliability, bias, transparency, and inclusivity.

Assessment Types

• Internal QA: Institutions ensure AI tools align with curriculum goals, privacy, and inclusivity.
• External QA: External reviewers assess fairness, explainability, and data protection compliance.

Who Evaluates AI Quality?

The Challenge

Traditional auditors may not fully understand algorithmic processes, data ethics, or AI bias.
Hence, quality in AI education requires cross-disciplinary evaluation—educators, data scientists, ethicists, and accessibility experts.

The Solution

Create AI Quality Boards within institutions to oversee:

• Ethical and technical validation
• Pedagogical relevance
• Student data protection

This ensures that evaluation is informed, fair, and context-sensitive. 

Why AI-Driven QA Matters

“Prevention rather than cure as the key to quality” – Deming (1986)

In AI contexts, prevention means training systems responsibly, monitoring algorithmic behavior, and ensuring student agency.
Key benefits include:

• Equity and inclusion through adaptive tools
• Efficiency via automation
• Transparency through explainable AI
• Trust in technology-supported learning environments

AI in E-Learning and Blended Learning

The New Challenge

E-learning platforms now integrate AI tutors, analytics dashboards, and automated feedback systems.
To maintain QA:

• Algorithms must be audited for bias
• AI feedback must be accurate and pedagogically sound
• Students must understand how AI tools operate

Principle

“AI should enhance, not replace, human pedagogy.”

Effective systems blend teacher expertise with AI insights—fostering a collaborative intelligence model.

AI Management in Education

“If the highest aim of a captain were to preserve his ship, he would keep it in port forever.”
— St. Thomas Aquinas

The A.I.M. Framework

A modern evolution of the PIVOT model for AI governance in education:

Element	Focus
A	Accountability – defining who oversees AI quality
I	Integrity – ensuring fairness, transparency, and inclusivity
M	Monitoring – continuously evaluating impact on learning outcomes

This empowers managers to integrate AI responsibly while maintaining institutional integrity.

AI Code of Practice Framework

A responsible AI code of practice should address:

Dimension	Description
Accessibility	Ensure AI tools are inclusive for all learners
Evaluation	Measure AI effectiveness using learning analytics
Supportive VLE	Integrate AI seamlessly within virtual learning environments
Collaboration	Promote human-AI collaboration among educators and learners
Transparency	Clearly explain AI’s role in decision-making

Conclusion

AI does not replace the human element of education—it augments it.
Quality management in the AI era means ensuring that technology enhances fairness, accountability, and learning impact, building an ecosystem of trust, inclusivity, and continuous improvement.