1. Live development of key task phases

By embedding verifiable, human-led processes into the early stages of a task, educators can maintain academic integrity without sacrificing flexibility in later phases. 

Certain critical stages of any task—whether brainstorming, outlining, drafting, or data collection—could be completed in controlled settings. This approach ensures that the foundational work is demonstrably the student’s own, creating a clear and verifiable starting point for further development. By anchoring these key phases in human-led processes, educators can foster genuine skill-building while minimising opportunities for undue assistance. 

These live sessions may take place in physical classrooms or, for digital tasks, within secure environments like lockdown browsers. During these periods, students work under direct observation, with access to AI or external resources restricted unless explicitly permitted. The goal is not to limit creativity but to establish a baseline of original effort that can be reliably assessed, and to provide a supportive structure for the ongoing development of the task as a whole.

Tangible evidence of the learning could be annotated drafts, timestamped notes, or other in-class artifacts that demonstrate the student’s progress. Such documentation not only verifies the authenticity of the work but also provides a clear record of development over time. 

Learning and Assessment Plans may clearly outline which phases require in-person or supervised completion (e.g., "First draft must be written in class without AI assistance"). This transparency ensures consistency, reduces ambiguity, and reinforces the value of independent effort. 

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2. "Live interview" sessions

Live interview assessment offers an alternative to traditional written expression, shifting the focus from static products to interactive, developmental conversations. These methods not only verify authenticity and deepen understanding but also provide immediate, actionable feedback. 

Live interview is best used when student understanding is to be assessed, rather than the authorship of a particular product.

Live interview itself can be done in a number of ways, can be designed to be brief and focused (such as a short 3-5 minute check in). Following are a few possible approaches:

Generative Interview 

The generative discussion is a formative, collaborative conversation aimed at guiding students through the development of their work. This method is used during the early or middle stages of a project, when the student’s ideas are still taking shape. The teacher’s role here is to act as a facilitator, helping the student clarify their thinking, align their work with assessment criteria, and identify next steps. 

During a generative discussion, the teacher and student engage in a dialogue about the student’s in-progress work, such as a draft, outline, or prototype. The teacher asks open-ended questions to prompt the student to articulate their learning process, such as, "What challenges are you facing with this section?" or "How does this part of your work connect to the assessment criteria?" 

The conversation is recorded, and the key points—including the student’s reflections, questions, and the teacher’s feedback—are captured (and potentially transcribed) to serve as a content for the next stage of development. This method not only scaffolds the student’s progress but also legitimises the interview process by directly tying it to the improvement of their work. 

Live annotation 

Live annotation is a real-time method that makes the student’s thought process visible by having them narrate their revisions while annotating their work. This approach is particularly useful for revealing how students arrive at their conclusions, identify gaps in their reasoning, or refine their ideas. 

In this method, the student annotates their work—such as a draft, model, or code—while explaining their decisions, revisions, and problem-solving strategies. For example, a student might stand at a whiteboard with their essay draft, crossing out sections, adding notes, and verbalising their thought process as they go. Alternatively, they might record a screen capture of themselves editing a digital document, narrating their changes in real time. 

The teacher or a peer listens and asks questions to uncover the student’s reasoning, such as, "Why did you choose to move this paragraph?" or "What alternative approaches did you consider?" , "To what extent did you use AI tools to develop this section?". This method is especially effective for complex tasks, as it reveals not just the final product but the iterative process behind it. 

Post-hoc verification interview 

The post-hoc verification interview is designed to confirm the students understanding after the development of a stage of the assessment. By engaging students in a dialogue about a product, the teacher can uncover the true extent of the student’s engagement with the material. 
In this approach, the teacher selects specific elements of the student’s work and asks probing questions to elicit explanations, justifications, and reflections. For example, a student who has submitted an essay might be asked to walk through their argument, explain their choice of evidence, or defend their conclusions. 

Responsive drafting

In addition to the three core methods, responsive drafting offers a powerful way to integrate live interview assessment into existing task development phases. This approach builds on the generative discussion or live annotation by providing targeted feedback after the student has fully explained their thought process. The key idea is to allow the student to articulate their reasoning first, without interruption, before the teacher responds with specific, actionable feedback. 

For example, during a generative discussion, the student might walk the teacher through their draft, explaining their choices, challenges, and next steps. Once the student has finished speaking, the teacher provides responsive drafting feedback—written or verbal notes that directly address the student’s explanations. This might include suggestions for refining their argument, clarifying their evidence, or restructuring their work. Similarly, in a live annotation session, the teacher might observe the student’s revisions and then provide feedback tailored to the student’s self-identified gaps or uncertainties. 

Responsive drafting ensures that feedback is personalised and context-specific, as it is based on the student’s own articulation of their work. It also reinforces the collaborative nature of the process, as the student sees their input directly shaping the teacher’s guidance. This method is particularly effective for students who struggle to get started or who benefit from immediate, concrete suggestions. By embedding responsive drafting into live interview assessment, teachers can create a seamless cycle of explanation, feedback, and revision that supports continuous improvement. 

Shared principles across Live Interview  

Although the student is the one speaking, the artefact should serve as the common ground and primary subject of discussion; ensuring the central focus stays on the artefact—the tangible product, project, or piece of work the student has produced—rather than on the student as an individual, can reduce the performance anxiety and allow for a grounded discussion of learning. 

Oral assessments are inherently dynamic, and their value lies in the ability to document the conversation for later reflection, moderation, and evidence. Whether through audio recordings, screen captures, or digital annotations, capturing the interaction ensures that the student’s thought process is preserved and can be revisited. This documentation is particularly useful for the student either as self-reflection or in developing the next phase of their task. It also provides a record for teachers to review, ensuring consistency in assessment and offering a basis for further feedback. 

Questions should be aligned with the assessment criteria, ensuring that the student’s responses demonstrate their understanding of the task’s requirements. For example, if the rubric emphasises the use of evidence, the teacher might ask, "How does this piece of evidence support your argument?" At the same time, questions should also push the student to engage with higher levels of complexity, such as evaluation or creation. For instance, the teacher might ask, "What assumptions are you making in this model, and how might they affect your conclusions?" By combining questions that address both the criteria and the depth of understanding, the teacher can gain a comprehensive view of the student’s learning. 

Finally, if comfortable with an interview method, you could consider customising a chat assistant to conduct the interview on your behalf, under supervision.

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3. Role-Play or simulation exercises

Role-play and simulation exercises don’t need to be full assessments—they may work best as short verification phases added to existing tasks. These quick checks reveal whether students can apply their knowledge in real-world scenarios, adapt to unexpected challenges, or communicate their ideas under pressure. By embedding them into familiar assessments, you can test deeper understanding without redesigning your entire evaluation system. 

The best time to add these exercises is after students have already shown foundational knowledge. For example, after a written task, ask students to defend one of their answers in a quick role-play with a skeptical peer. After a group project, have students role-play a negotiation or conflict resolution based on their work. These moments don’t need to be long; even 5–10 minutes can provide valuable insights into their ability to think critically and adapt. 

To design these verification phases, focus on one or two key skills from the original assessment. The scenario should feel realistic but manageable—students shouldn’t be overwhelmed, but they should have to think on their feet. Provide clear instructions and define what you’re assessing, such as adaptability, communication, or problem-solving. 

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4. Time-bound, in-class assessments

Students complete a structured, time-limited task—such as writing, problem-solving, or live performance—under direct supervision, either in person or virtually with monitoring. No AI tools or external resources are permitted unless explicitly allowed (e.g., calculators, pre-approved notes). 

Key rules ensure fairness and rigor. Strict time limits (e.g., 40 minutes) create pressure while maintaining consistency. Resources like case studies or formulas may be provided on paper or digitally, but students must rely solely on their own knowledge and skills. Evidence of completion includes the final product and timestamped submissions.

This approach works because it ensures adaptability in a live context. It also tests higher-order skills—adaptability, synthesis, and application—under real-world pressure, mirroring scenarios professional presentations. By prohibiting pre-planned content,  it forces students to engage critically in the moment. 

For compliance with learning assessment policies (LAP), instructors must clearly state the time limit, allowed resources, and AI restrictions (e.g., "40-minute in-class response; Specified teacher created chat assistant only"). 

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5. Supportive assessment design features

While not formal assessment conditions, the following features may be useful and appropriate in making the learning process visible to support independent teacher verification of learning

• Requiring process documentation at key moments throughout the development of a task can improve the visibility of the learning process. Have students maintain a journal or logbook that records their steps or ask students to submit work-in-progress notes such as brainstorming, outlines, and reflections to show their thinking journey. 
• Integrate process evidence: increase the value of drafts, reflections, annotated portfolios, or artefacts created during class or in supervised contexts.
• Focus on unique or local contexts: reference class discussions, case studies, school projects, or recent learning experiences that can’t be anticipated by AI. 
• Negotiate personalisation: where possible, allow students to select topics or tasks relevant to their lives, interests, or local context—further increasing authenticity and uniqueness. Here, using AI tools to support brainstroming may be useful, to encourage divergent thinking and considering options.
• Design for analysis, evaluation, and creation: prioritise tasks that require students to analyse scenarios, justify choices, adapt processes, reflect on their approach, or create original responses 
• Encourage metacognition: require written or oral reflection on how they learned or solved a problem, including challenges faced and strategies used. 
• Multi-modal evidence: ask for combinations of visual, oral, and written evidence to demonstrate understanding in different formats and modes of expression.
• Use a variety of assessment activities to assist in detecting anomalies. Different types of tasks allow teachers to observe a student’s typical style, strengths, and challenges. e.g. course work, group discussions, fieldwork, practical or laboratory activities, research, oral/multimodal presentations, short tests or quizzes. 
• Conduct check-ins to gauge student understanding and confirm authorship. e.g. during class, have 5-minute one-on-one chats where students explain their project focus and how they’re approaching the task. Note any discrepancies in understanding or fluency. 
• Require process documentation at key moments throughout the development of a task. Have students maintain a journal or logbook that records their steps, or ask students to submit work-in-progress notes such as brainstorming, outlines, and reflections to show their thinking journey. 
• Embed peer review and collaboration as part of the assessment response. e.g. have students exchange drafts and provide documented feedback, which can then be used in the verification process where a student adopts aspects of the feedback provided.  
• Create a safe space for questions and disclosure such as by:
  • Foster transparency by encouraging students to ask about AI use and disclose their methods without fear of punishment.
  • considering a Q&A box in your classroom where students can anonymously submit questions
  • sharing prompts students may ask of AI and consider where they are/are not helpful for deep thinking
  • Modelling the effective use of AI in the classroom, demonstrating subject specific applications
  • Regularly discussing AI hallucinations or issues with responses
• Set tasks that incorporate student reflection and analysis of information rather than fact or information gathering. Where appropriate to the subject outline requirements, shift focus from product to process, rewarding originality, reasoning, and reflection. e.g. as part of the task, ask for “evidence of original thinking”, “reflection on process”, and “ethical use of tools,” alongside content and structure. 
• Have students complete an academic honesty statement. Require students to declare the originality of their work and disclose any use of AI tools. e.g. Students complete a declaration at the end of each assignment "“I confirm that this work is my own and that I have disclosed any use of outside sources including AI tools”. 
• Teach students about academic integrity, plagiarism and proper citation. Explicitly guide students through the SACE Board’s academic integrity policy. Plagiarism may be unintentional and be the result of poor citation practices. Provide feedback on proper referencing and academic integrity and offer the opportunity to revise and resubmit work. 

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