Artificial Intelligence Use and Disclosure Policy

1. Purpose

IWS Online School recognises that artificial intelligence is becoming an increasingly important part of education, curriculum development, research, assessment support, data analysis, and professional practice.

Our approach is to use AI responsibly as a development, analytical, and support tool while maintaining clear human academic oversight, professional accountability, quality assurance, and appropriate data governance.

AI is not used as a replacement for qualified teachers, academic judgement, or professional responsibility. Instead, it is incorporated into structured processes designed and supervised by experienced education professionals.

2. Our Approach to AI

IWS does not consider AI-generated and human-generated content to be mutually exclusive.

When used appropriately, AI can support greater consistency, breadth, synthesis, efficiency, personalisation, and scalability across curriculum development and student support.

However, IWS does not rely on simple, unstructured prompts to generate educational content for direct publication or delivery to students.

AI is used within detailed, human-designed academic and pedagogical frameworks and is subject to professional review before content is published, used in teaching, or applied to student support.

AI may assist with development, analysis, monitoring, and recommendations. Academic review, quality assurance, approval, and final decision-making remain human responsibilities.

3. Human-Designed Academic Framework

The foundations of IWS courses are designed by education professionals.

Our course-development framework contains detailed guidance covering, among other areas:

• syllabus requirements;
• curriculum coverage;
• assessment objectives;
• subject-specific requirements;
• course and lesson architecture;
• instructional design standards;
• sequencing and progression;
• learning objectives;
• assessment preparation;
• Universal Design for Learning (UDL);
• the 5E instructional model;
• Bloom’s Taxonomy;
• student accessibility and engagement;
• differentiation;
• academic quality standards; and
• expectations for teacher review and approval.

AI tools are used within these defined parameters rather than operating independently of them.

4. Role of AI in Course Development

AI may be used to support aspects of course-content development, including:

• drafting;
• structuring;
• synthesising;
• adapting;
• differentiating;
• refining;
• checking consistency;
• generating examples;
• supporting accessibility;
• producing alternative explanations or formats; and
• assisting with curriculum mapping and content review.

The role of AI is to support the development process within the academic standards, syllabus requirements, and instructional framework established by IWS.

AI-generated output is not automatically accepted, published, or delivered to students.

All course content remains subject to human review, academic judgement, quality assurance, and approval.

5. Qualified Teacher Review and Approval

Before publication, course content is reviewed by qualified teachers with relevant subject expertise.

The review process considers areas including:

• factual and academic accuracy;
• syllabus alignment;
• coverage of learning and assessment objectives;
• subject appropriateness;
• sequencing and progression;
• level of challenge;
• clarity and accessibility;
• pedagogical quality;
• suitability for the relevant age and qualification level; and
• overall value as a teaching and learning resource.

Course materials are published only after the relevant review and approval process has been completed.

Qualified teachers therefore retain responsibility for determining whether content is academically appropriate and suitable for use within the programme.

6. Live Teaching and Teacher Professional Judgement

Published course content provides a structured foundation for teaching, but it does not replace the role of the teacher.

Teachers delivering live lessons are expected to use their professional judgement to adapt, supplement, contextualise, and tailor materials according to the needs of the students they teach.

Teachers may create additional:

• live lesson materials;
• explanations;
• activities;
• examples;
• revision resources;
• formative assessment tasks;
• extension work;
• intervention resources; and
• differentiated learning activities.

These materials are informed by the approved course content and by the individual needs, progress, and engagement of students.

The educational experience therefore combines structured digital content, teacher-led instruction, professional judgement, and personalised academic support.

7. AI-Supported Personalisation and Student Progress Analysis

IWS may use AI-supported tools to analyse student learning data within the course environment.

This may include information relating to:

• academic progress;
• assessment performance;
• engagement;
• participation;
• completion patterns;
• areas of strength;
• gaps in understanding;
• learning pace;
• outcomes; and
• indicators that a student may require additional support.

The purpose of this analysis is to help teachers and academic teams identify individual learning needs earlier and more accurately.

AI-supported analysis may contribute to the development of:

• Personalised Learning Plans (PLPs);
• tailored learning activities;
• differentiated resources;
• targeted revision materials;
• intervention strategies;
• recommendations for additional tutoring;
• personalised teacher approaches;
• additional academic support; and
• closer progress monitoring.

Our objective is to move beyond a one-size-fits-all model of education and provide a learning experience that is more responsive to each student's individual progress, needs, strengths, and development.

AI may help identify patterns, generate insights, and support recommendations. It does not independently make final academic, teaching, progression, or intervention decisions.

Review, interpretation, quality assurance, approval, and decision-making remain the responsibility of qualified teachers, relevant functional leads, and the IWS leadership team.

Human professional judgement remains central to how student information is interpreted and how educational action is taken.

8. Personalised Learning and Intervention

IWS evaluates the quality of education not only through the content provided, but also through student progress, engagement, development, and outcomes.

Through our teaching, mentoring, and academic monitoring systems, we seek to identify when a student:

• is falling behind;
• is underperforming;
• is disengaging;
• requires greater challenge;
• needs additional explanation;
• would benefit from a different teaching approach; or
• requires targeted academic intervention.

Where appropriate, IWS may introduce measures such as:

• Personalised Learning Plans;
• additional tutoring;
• mentoring support;
• differentiated learning materials;
• targeted academic interventions;
• adapted teaching approaches;
• revision programmes; and
• additional progress monitoring.

This model is intended to ensure that educational provision responds to the student rather than expecting every student to follow an identical learning pathway.

9. Course Structure and Examination Board Alignment

IWS courses are designed to ensure that the relevant syllabus requirements, subject content, learning objectives, and assessment objectives are fully covered.

The internal structure or numbering of units within the IWS platform may not always mirror the numbering used by an examination board.

Where this occurs, it is an intentional course-design decision intended to provide a clearer, more coherent, and student-friendly learning architecture.

Our priority is to ensure appropriate sequencing, curriculum coverage, progression, and assessment preparation rather than requiring internal course numbering to replicate an examination board's numbering exactly.

Differences in numbering do not indicate that syllabus requirements have been omitted.

10. Human Oversight and Accountability

The use of AI does not remove human accountability.

IWS retains responsibility for the academic standards of the courses it publishes, the quality of the education it provides, and the decisions made in relation to students.

AI may support:

• content development;
• data analysis;
• progress monitoring;
• pattern identification;
• recommendations;
• differentiation;
• planning; and
• quality assurance activities.

However, final responsibility for academic review, approval, teaching decisions, intervention decisions, and quality assurance remains with appropriately qualified and authorised members of staff.

Depending on the nature of the decision, this may include:

• qualified subject teachers;
• mentors;
• academic staff;
• functional leads;
• quality assurance teams; and
• the IWS leadership team.

AI is therefore used to augment professional expertise rather than replace it.

11. Quality Assurance

IWS is committed to continuously reviewing and improving its curriculum-development, teaching, and student-support practices as technology, educational standards, examination requirements, and teaching methodologies evolve.

Course materials and educational processes may be reviewed or revised following:

• teacher feedback;
• student performance data;
• engagement data;
• curriculum or syllabus updates;
• examination or assessment changes;
• academic quality reviews;
• student outcomes;
• internal audits;
• developments in educational research; and
• developments in educational technology.

AI may support aspects of this quality-assurance process by identifying patterns, inconsistencies, gaps, or opportunities for improvement.

Final quality-assurance decisions remain subject to human review and approval.

12. Data Privacy and Responsible Use

Where AI-supported tools are used to analyse student information, IWS is committed to using such tools responsibly and in accordance with applicable data-protection, privacy, and safeguarding requirements.

Student data should only be used for legitimate educational, operational, safeguarding, or quality-assurance purposes.

Access to student information should be appropriately controlled and limited to authorised individuals and systems.

Where third-party technology providers are used, IWS seeks to ensure that appropriate safeguards, contractual controls, and data-handling standards are in place.

AI-supported analysis of student information is intended to improve educational provision and support individual learning. It should not be used as the sole basis for significant academic or educational decisions about a student.

Where appropriate, human review must take place before action is taken.

13. Transparency

IWS is committed to being transparent about the responsible use of artificial intelligence within its educational development and student-support processes.

Where AI has been used as part of course development, analysis, personalisation, or quality assurance, this should be understood as one component of a broader human-led process involving:

• curriculum design;
• academic standards;
• qualified teacher review;
• quality assurance;
• approval;
• live teaching;
• mentoring;
• student progress monitoring;
• personalised support; and
• leadership oversight.

Our position is therefore not that educational content or educational processes should be classified simply as either “AI-generated” or “human-generated”.

Rather, our model is based on responsible technology-assisted education with qualified human oversight and accountability at every critical stage.

14. Responsible Use Principles

IWS seeks to apply the following principles when using AI:

Educational purpose
AI should be used where it supports learning, teaching, quality, personalisation, efficiency, or student outcomes.

Human oversight
AI-supported content, analysis, and recommendations remain subject to appropriate human review.

Academic integrity
AI use should support, rather than undermine, academic standards, syllabus requirements, and educational objectives.

Quality assurance
AI-generated or AI-supported output should not be assumed to be accurate solely because it was produced by an advanced system.

Proportionality
AI should be used where it adds genuine educational or operational value.

Transparency
IWS should be able to explain, at an appropriate level, how AI is used within its educational model.

Privacy and safeguarding
Student information should be handled responsibly and in accordance with applicable privacy and safeguarding obligations.

Human accountability
Responsibility for educational decisions remains with IWS and its authorised staff.

15. Continuous Review

AI technology and its role in education continue to evolve rapidly.

IWS will therefore review its use of AI periodically to ensure that its practices remain aligned with:

• educational standards;
• legal and regulatory requirements;
• safeguarding expectations;
• data-protection obligations;
• examination-board requirements;
• developments in teaching and learning; and
• the best interests of students.

Where appropriate, this policy may be updated to reflect changes in technology, regulation, educational practice, or IWS systems and processes.

16. Commitment to Educational Quality

IWS remains committed to the quality of its curriculum, teaching, academic support, and student outcomes.

Our primary objectives are:

• high-quality teaching and learning;
• comprehensive syllabus coverage;
• appropriate preparation for assessment;
• measurable student progress;
• strong academic outcomes;
• personalised learning;
• early identification of learning needs;
• targeted academic support;
• continuous quality improvement; and
• responsible use of technology in education.

AI is used in support of these objectives.

It is not a substitute for professional teaching, academic expertise, human judgement, or institutional responsibility.