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Traditional technical interviews often face challenges in providing a consistent and accurate assessment of candidates' skills. Human interviewers, despite their expertise, may introduce bias or inconsistencies into the process due to factors like fatigue, subjectivity, or varying standards. Moreover, interviews typically do not adapt in real-time to a candidate's performance, leading to either overly challenging or insufficiently challenging questions that do not accurately reflect the candidate's abilities.
AI interview systems have the potential to address these issues by offering a standardized and objective assessment process. However, most existing systems lack the ability to adapt dynamically to the candidate's skill level, which is crucial for an accurate evaluation. Additionally, the absence of human-like interactions, such as hand gestures and facial expressions, can make AI-driven interviews feel impersonal and intimidating, potentially affecting the candidate’s performance.
This project aims to overcome these challenges by developing an AI interviewer that not only adapts its questioning strategy in real-time but also engages candidates through realistic animations and gestures. By simulating human-like interactions, the system seeks to create a more comfortable and natural interview environment, leading to more accurate assessments and a better overall experience for candidates.
Several assumptions were made to guide the development and testing of the AI-driven technical interviewer:
• User Interaction: It is assumed that users will interact with the AI interviewer through a standard web interface, either on a desktop or mobile device. The interface will be intuitive and user-friendly, minimizing the need for prior training.
• Real-time Processing: The AI is assumed to be capable of processing and analysing user responses in real-time, allowing for immediate adjustments in the difficulty of questions. This requires robust natural language processing (NLP) capabilities.
• Enhanced Engagement through Animations: It is assumed that integrating animations and hand gestures into the AI's responses will enhance user engagement and make the interview process feel more natural, similar to a human interaction.
The experimental work focuses on building the AI system, integrating animations, and testing the effectiveness of the adaptive questioning algorithm:
• Development: The system was developed using a combination of machine learning models for question generation and NLP for understanding user responses. Tailwind CSS and JavaScript libraries were employed to create the animated interviewer interface.
• Data Collection: Data was collected during user interaction with the AI interviewer, capturing various metrics such as response times, accuracy of answers, user satisfaction scores, and engagement levels. Feedback from users regarding the realism of animations and the perceived difficulty of questions was also collected.
• Testing Environment: The system was tested with users of varying technical backgrounds, ranging from beginners to experts, to evaluate its adaptability and effectiveness across different skill levels.
The results of the experimental phase demonstrated the effectiveness of the AI system:
• User Performance: Metrics showed that the AI could successfully adapt to the user’s skill level, providing appropriately challenging questions. Users reported high levels of satisfaction with the system’s ability to accurately gauge their technical abilities.
• Engagement: Users were notably more engaged when animations and gestures were included, with feedback indicating that these features made the interaction feel more personal and less mechanical.
• Illustrations: Diagrams and screenshots of the AI interface and examples of the animation sequences were included in the report to illustrate how the system operates in practice.
This is a Next.js project bootstrapped with create-next-app.
First, run the development server:
npm run dev
# or
yarn dev
# or
pnpm dev
# or
bun devOpen http://localhost:3000 with your browser to see the result.
You can start editing the page by modifying app/page.js. The page auto-updates as you edit the file.
This project uses next/font to automatically optimize and load Inter, a custom Google Font.
To learn more about Next.js, take a look at the following resources:
- Next.js Documentation - learn about Next.js features and API.
- Learn Next.js - an interactive Next.js tutorial.
You can check out the Next.js GitHub repository - your feedback and contributions are welcome!
The easiest way to deploy your Next.js app is to use the Vercel Platform from the creators of Next.js.
Check out our Next.js deployment documentation for more details.