CAPTURE

A new recruitment approach 

In today’s diverse society, many job seekers struggle to enter the workforce. A major obstacle is the complexity of digital job applications, which can be difficult to navigate.

The CAPTURE project aims to break down these barriers and promote diversity and inclusion in hiring. The goal is to build and use a human-centered, explainable AI-powered voicebot that simplifies the job application process. This voicebot will act as a digital partner for jobseekers, interacting with candidates in natural conversations, assessing their skills and competencies without unnecessary complexity or bias.

At its core, the voicebot will help match candidates with suitable roles based on key criteria and relay this information to the recruiter. Recruiters can provide feedback to the voicebot, improving the accuracy of job recommendations and ensuring they better align with the jobseeker’s profile.

More than just improving efficiency, CAPTURE seeks to redefine recruitment. Instead of focusing on company needs first, it shifts the approach to a candidate-centered one, ensuring that job seekers feel understood, supported, and fairly evaluated throughout the hiring process.

An inclusive recruitment that reduces entry barriers

The project will design a voicebot that understands cultural preferences and communicates with empathy and respect. The bot will implement an ethical framework that is transparent and fair. The project will also design explanation methods to assist recruiters and other stakeholders in understanding job seekers' competency profile and in identifying biases. It does so by incorporating a human-in-the-loop approach for better decision-making transparency and fairness.

Adding a candidate-centric approach within a robust competency framework

CAPTURE aims to implement a candidate-first approach, supported by a well-researched competency framework. That framework is based on global best practices, ensuring that AI-suggested matches align with expert evaluations, and prioritizing candidate experience to enhance engagement. Additionally, it includes rigorous bias testing to ensure a fair and equitable treatment of all candidates across diverse backgrounds.

Including conversational intelligence for automated pre-screening

The voicebot must tackle three main challenges: (1) adapting question sequences dynamically, (2) personalizing dialogue flow and language, and (3) boosting engagement to reduce candidate drop-out.

To achieve this, CAPTURE integrates rule-based voicebot technology with generative AI. This includes automatically identifying conversation intents, tailoring dialogues based on conversation history and external data, adapting responses to user emotions for a more empathetic experience, and controlling vocabulary to match candidate literacy and job-specific terminology.

Expected Outcomes

By implementing these objectives, the project aims to deliver:

• A scalable and efficient AI system optimized for real-world hiring scenarios
• A multilingual AI voicebot for inclusive job applications
• An explainable, bias-mitigated human-centred AI for fair candidate evaluation
• A robust, competency-based job-matching framework

“CAPTURE makes job applications simpler, fairer, and more inclusive. By using human-centered, explainable AI, it ensures job seekers are assessed on skills, not barriers. This shifts recruitment from a company-first to a candidate-first approach, fostering diversity and equal opportunity.”