An AI-powered web tool that helps students practice for interviews by recording their responses and providing structured feedback. The AI analyzes spoken answers for clarity, structure, relevance, speaking speed, and even eye contact via webcam.
University and TAFE careers services know that interview practice makes a real difference, particularly for first-generation students, international students and graduates entering competitive programs. But one-on-one mock interviews with a careers adviser are limited by staff time, and many students feel anxious about booking one. They go into graduate interviews having never heard themselves answer a behavioural question aloud, let alone received specific feedback on how they came across.
An AI interview practice tool gives students unlimited, low-pressure rehearsal. They choose a role type, answer recorded questions through their browser, and receive structured feedback on answer structure, relevance, clarity, speaking pace and filler words, with examples from their own transcript. Optional webcam analysis can comment on framing and eye contact, but that feature needs particular care because non-verbal norms vary by culture and disability. The tool supplements advisers, who can review sessions students choose to share.
The student logs in, chooses a role or industry, and receives a set of behavioural and technical questions from a bank curated by careers advisers.
Answers are recorded in the browser using WebRTC, and speech-to-text, such as Whisper, produces a timed transcript of each response.
A language model assesses the transcript against frameworks like STAR, checking whether the answer covered situation, task, action and result and stayed relevant to the question.
Speech metrics such as pace, pauses and filler words are calculated, and if the student opts in, MediaPipe estimates camera framing and gaze direction.
The student receives a scorecard with examples and suggestions, can retry the question immediately, and may choose to share the session with a careers adviser.
A university career center integrates the tool into their platform. A student practices for a behavioral interview, and the AI provides a scorecard showing they spoke too quickly and used too many filler words, with specific examples and tips for improvement.
React/Next.js, Python/Node.js, WebRTC, Speech-to-Text (Whisper), NLP (GPT), Computer Vision (Mediapipe)
Student voice and video recordings are personal information under the Privacy Act 1988 or applicable state privacy law, and facial analysis data may be treated as sensitive biometric information, so make video analysis opt-in, avoid storing face data and set short retention periods. The Disability Discrimination Act 1992 means the tool should not penalise students for stuttering, low vision, autism-related eye contact differences or other disabilities; offer alternatives and let students switch off metrics. Many institutions require data to stay in Australian cloud regions and expect a privacy impact assessment before rollout. Make clear to students that feedback is advisory, not a prediction of hiring outcomes.
This solution requires careful evaluation of cost-benefit ratio. Consider implementation only after thorough analysis and risk assessment.
No, and it should not claim to. It gives feedback on how clearly and completely a student answered, not on employability. Real hiring decisions involve many factors the tool cannot see. Positioning it as a practice partner avoids giving students false confidence or unnecessary discouragement.
It is a full web application with real-time recording, speech processing, LLM feedback, optional computer vision, single sign-on, accessibility work and privacy review. Careers advisers also need time to write question banks and test feedback quality with students before launch.
Yes, and many will prefer to. Audio-only practice still provides feedback on structure, relevance, pace and filler words, which is where most improvement comes from. Webcam analysis should be clearly optional and switched off by default.
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