Artificial Intelligence (AI) case studies

Case Study 1: Ensuring integrity in AI supported assessment  

A Registered Training Organisation (RTO) identified a concern during assessment marking for BSBCRT411 Apply critical thinking to work practices. A student submitted a written assessment that differed significantly from previous work, using advanced language, sophisticated reasoning frameworks and detailed examples not explicitly covered during training.

An AI detection tool indicated that the work was likely generated or heavily influenced by artificial intelligence (AI). When questioned, the student explained they had used an AI tool to refine their draft, improve sentence structure and enhance the quality of their writing. The student stated that the ideas and analysis were their own but acknowledged uncertainty about whether this use of AI was permitted.

As BSBCRT411 focuses on analysing, evaluating and justifying thinking processes, the trainer and assessor determined that written evidence alone was insufficient to verify authenticity. Rather than treating the matter as misconduct, they used an alternative assessment approach to confirm competency.

The student was asked to submit a video response addressing prompts aligned to the unit requirements, including:

• explaining the workplace problem or scenario they analysed
• describing how they gathered and evaluated information
• outlining the critical thinking process used to assess options
• justifying their chosen solution
• reflecting on how they reviewed and improved their thinking practices.

The student recorded and uploaded the video using an approved AI supported platform that enabled identity verification and secure submission. The response demonstrated a clear understanding of the required concepts, including problem identification, evaluation of alternatives and decision-making. While the student's verbal explanations were less sophisticated than the written submission, they successfully demonstrated the required competency. The contrast indicated that the student had relied heavily on AI to enhance the presentation and sophistication of their written work.

The trainer provided targeted feedback, including:

• using AI to structure drafts or improve clarity, rather than generate or replace personal analysis
• ensuring all reasoning, evaluation and justification reflect the student's own thinking
• being prepared to explain and defend ideas verbally
• disclosing AI use where required.

The RTO also reviewed its assessment practices for units where demonstrating thinking processes is critical. Improvements included:

• providing clear instructions on acceptable AI use in assessment tasks
• incorporating verbal or multimodal assessment methods to confirm authenticity
• designing tasks that require personal reflection and justification
• supporting students to develop AI literacy alongside critical thinking skills.

This case highlights that, in critical thinking units, how a student develops and explains their reasoning is as important as what they write. By combining written and verbal evidence, the RTO maintained assessment integrity while supporting responsible AI use and authentic demonstration of competency.

Case Study 2: Supporting inclusive and effective use of AI in training and assessment

A Registered Training Organisation (RTO) delivering the unit BSBMKG433 Undertake marketing activities introduced an AI-powered campaign tool into learning activities. The tool generated campaign analytics, draft social media content and audience profiles, helping to align training with current and emerging industry practices.

Initially, trainers used AI outputs effectively as discussion starters. Students critiqued messaging, assessed audience targeting and evaluated campaign effectiveness, supporting the unit’s focus on analysing marketing activities and refining approaches.

Over time, however, the AI outputs increasingly replaced trainer-led demonstrations and explanations. This reduced opportunities for students to observe how a trainer with industry experience interprets campaign data, applies judgement and refines strategies beyond automated outputs.

The tool was later embedded into an assessment task, requiring students to generate a campaign draft, refine it according to brand guidelines, and justify their decisions regarding target audience, marketing objectives and anticipated campaign results. Some students performed well. Others, particularly those with lower digital confidence or accessibility needs, found the platform difficult to navigate and struggled to interpret outputs. This created barriers to demonstrating competency.

Following a review, the trainer and assessor identified that the issue was not AI itself, but its implementation without sufficient support, context or flexibility. To improve practice, the RTO repositioned the AI tool as a support resource rather than a primary source of instruction. Trainers reintroduced explicit teaching and modelled how to:

• question the relevance and accuracy of AI-generated analytics
• identify gaps or assumptions in audience profiles
• compare AI outputs with real campaign examples
• adapt and refine content using professional judgement.

Structured support was also introduced through guided instruction, practice activities, feedback and collaborative discussions. These strategies helped students build confidence and prepare for assessment.

The assessment task was redesigned to improve accessibility and fairness while maintaining alignment with unit requirements. Students could still use the AI tool, but alternative options were introduced. This included:

• analysing provided campaign materials instead of generating new ones
• using templates and guided prompts to structure responses
• accessing clear instructions outlining the purpose and limits of AI use.

To ensure authentic evidence of competency, students were also required to briefly explain their campaign decisions, including how they interpreted and adapted any AI-generated content. This ensured assessment focused on the student’s understanding, analysis and decision-making, not just the final product.

At an organisational level, the RTO provided guidance on appropriate AI integration, embedding AI literacy into training, supporting students with varying digital capabilities and ensuring multiple ways to demonstrate competency. By combining AI-supported activities with explicit teaching and inclusive assessment design, the RTO ensured that all students could engage meaningfully and demonstrate their competency. This demonstrates that effective AI use in training depends on balancing innovation with instructional support and equitable access, so technology enhances learning rather than limits it.