Situational Judgement Tests (SJTs) or Ai interview automation?
From one recruiter to another and one employer to another, the ways candidates are selected vary greatly. But ask anyone involved in the process, and most will agree that what happens at the early candidate screening stage, is critical to getting the best outcomes. Traditionally, it’s also been the most time-consuming and costly part of the hiring process.
Long before a face-to-face interview, recruiters need to screen candidates to decide, from potentially thousands of applicants, who should proceed to the next steps in the hiring cycle. But before they’ve even met a candidate, can recruiters really assess someone’s ability and suitability for the job they’re applying for? Yes, they can, especially with tools like the situational judgement test.
Choosing the best assessment solution for your recruitment tools suite.
In contemporary recruiting, a suite of tools and technologies can help take the hard work and the guesswork out of the hiring process. Talent assessment tools, like situational judgement tests for managers or situational judgement tests for customer service, help recruiters identify the best candidates faster – talent who will be the best fit for the role and the team, work most productively and stay in the role longer.
While traditionally a time-consuming manual review of applications and CVs would begin the hiring process, recruiters have embraced technologies that can automate these processes from the outset.
In this article, we compare two top of the funnel tools recruiters are using to assess candidates: traditional situational judgement tests (SJTs) and the next generation text interview platform.
Sapia Ai-enabled automated interviews could provide the answers you’re looking for, helping to connect to the best talent faster and more cost-effectively.
So, what is a situational judgement test?
Situational judgement tests are used to assess a candidate’s judgement and ability to respond appropriately to the real-world situations they would be likely to encounter in the workplace.
Candidates are presented with a workplace scenario and then they are required to choose or rank the best (or worst) paths to resolve the challenge, conflict or opportunity. They are a type of psychological aptitude test that provides insight and assessment of a candidate’s job-related skills.
What do situational judgement tests measure?
While the challenging scenarios presented to candidates are hypothetical, the best tests are designed around the role they are applying for.
Reflecting real situations they could encounter, the scenarios may involve working with other team members or supervisors, interacting with customers or dealing with day-to-day challenges.
Situational judgement tests date back to the 1940s. While the ways they are delivered may have changed, they remain a popular way to assess skills such as problem-solving and interpersonal skills. They are also useful in assessing soft skills and practical, non-academic intelligence.
Situational judgement tests are customised to the role and the organisation. Generally, they would be looking to assess a candidate’s aptitude for a role by measuring competencies that might include:
- Communication skills – clarity, persuasiveness, empathy
- Organisation and planning – solving the problem, staying cool under pressure
- Teamwork – collaboration, encouraging others, prioritising team needs over the individual, implementing solutions
- Decision making – exercising discretion, analysing the situation, demonstrating solid judgment
- Customer focus – listening, recognising, delivering
- Initiative – taking responsibility, demonstrating leadership, stepping up
- Ambition – drive to achieve
How does a situational judgement test work?
As they are produced by a range of different providers, SJTs can be delivered in a number of ways. As they are also tailored to suit specific roles and companies, tests can vary in their length, structure and format. While some may be paper-based, most tests are delivered digitally.
The tests provide candidates with a workplace scenario – as a written description or as a video or digital animation – and a challenge related to that scenario. Typically, candidates are then presented with four or five possible paths of action in multiple-choice format to deal with the situation described.
Different approaches are used for candidates to provide their answers. Some may require candidates to choose both the most desirable and the least desirable action. Others may ask candidates to choose just one preferred option or rank all actions in terms of effectiveness.
What benefits can SJTs bring to the hiring process?
Situational judgement tests are typically used before the interview stage and often used in combination with a knowledge-based test.
SJTs are designed to help recruiters and hiring managers to:
- filter candidates from large talent pools
- identify candidates who are likely to perform best in the role
- provide candidates with further insight into the demands of the role
- identify candidates who will be a good cultural fit
- assess candidates’ aptitude and judgement against realities of the role
- understand a candidate’s aptitude for the particular job
- help reduce staff turnover by making more informed decisions
Sapia – the smarter way to assess candidates
Since 2013, Australian recruitment technology specialist Sapia has worked to solve a problem for every recruiter and employer. That is how to get to the right talent faster while consistently improving the candidate experience.
Sapia’s text-based interview platform uses artificial intelligence, machine learning and natural language processing to provide reliable personality insights into every candidate. While SJTs can be expensive time-consuming to create, administer and assess, Sapia’s platform can provide like-for-like personality and job-fitness tests with far greater ease and at a fraction of the cost.
Why organisations are turning to interview automation over situational judgement tests
Here is feedback from a customer after running a pilot using SJTs:
Often situational judgement tests don’t accurately represent what the job is really about. There are so many aspects that need to be considered within a real-world situation. Feedback from the SJTs pilot groups is that they often felt as though they were being forced into specific areas that may not be job-related. There needs to be more flexibility for a candidate to say: “I would do this, but I would also do a bit of that”. Having an experience that gives flexibility in answering. It enables candidates to have that open-ended answer to express what was important to them.
How Sapia’s interview automation works
Smart Interviewer is Sapia’s machine learning interview platform. With learning from analysing more than 165 million words in text-based interviews with more than 700,000 candidates, Smart Interviewer combines standard interview questions related to past behaviour and situational judgement to reliably assess personality traits. The questions can be customised to the specific role family – sales, retail, call centre, service etc– and specific requirements relating to the employer’s brand and employment values.
Candidate assessment at scale
The scientific foundation of Sapia’s Ai interview platform is that language forms the framework for the knowledge, skills and personality we possess. Through a simple text-based conversation, Smart Interviewer provides valuable candidate insights. It can predict a candidate’s suitability for a role and guide their progression through the recruitment process. It delivers the insights that recruiters and employers need to make better hiring decisions at scale.
Enhancing the candidate experience – 99% satisfaction
Improving the candidate experience is a priority for every recruiter and employer. The effect of a poor experience can cause lasting damage to reputations and brands. Sapia is the only conversational interview platform with 99% candidate satisfaction feedback. Candidates enjoy the process, appreciate the opportunity and value the personalised feedback. Something that’s simply not practical with most high-volume recruitment briefs.
Candidates know text and trust text
As text is a familiar, non-confrontational way to connect, candidates enjoy the text interview experience. Unlike SJTs that lock them into choosing options from pre-determined answers, candidates appreciate the open-ended questions . Here they are empowered by the opportunity to tell their story in their words.
While questions are customised to the role, some typical examples include:
• What motivates you? What are you passionate about?
• Not everyone agrees all the time. Have you had a peer, teammate or friend disagree with you? What did you do?
• Give an example of a time you have gone over and above to achieve something. Why was it important for you to achieve this?
• Sometimes things don’t always go to plan. Describe a time when you failed to meet a deadline or personal commitment. What did you do? How did that make you feel?
• In sales, thinking fast is critical. What qualifies you for this? Provide an example.
Tackling bias and taking CVs out of the equation
Sapia provides blind-screening at its best, effectively reducing opportunities for bias from the assessment process to ensure every candidate is playing on a level field. Candidates recognise and appreciate the opportunity to tell their story without the subjective biases of a human interview or a cursory review of their CV. For top of the recruitment funnel interviews, Sapia removes CVs from the process altogether.
Find out more about Sapia’s Ai-powered candidate assessment tool and how it could replace your time-consuming and costly SJTs today.
You can leave us your details to get a personalised demo OR try out Sapia’s Chat Interview right now, here.
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Blog
New Research Proves the Value of AI Hiring
A new study has just confirmed what many in HR have long suspected: traditional psychometric tests are no longer the gold standard for hiring.
Published in Frontiers in Psychology, the research compared AI-powered, chat-based interviews to traditional assessments, finding that structured, conversational AI interviews significantly reduce social desirability bias, deliver a better candidate experience, and offer a fairer path to talent discovery.
We’ve always believed hiring should be about understanding people and their potential, rather than reducing them to static scores. This latest research validates that approach, signalling to employers what modern, fair and inclusive hiring should look like.
The problem with traditional psychometric tests
While used for many decades in the absence of a more candidate-first approach, psychometric testing has some fatal flaws.
For starters, these tests rely heavily on self-reporting. Candidates are expected to assess their own traits. Could you truly and honestly rate how conscientious you are, how well you manage stress, or how likely you are to follow rules? Human beings are nuanced, and in high-stakes situations like job applications, most people are answering to impress, which can lead to less-than-honest self-evaluations.
This is known as social desirability bias: a tendency to respond in ways that are perceived as more favourable or acceptable, even if they don’t reflect reality. In other words, traditional assessments often capture a version of the candidate that’s curated for the test, not the person who will show up to work.
Worse still, these assessments can feel cold, transactional, even intimidating. They do little to surface communication skills, adaptability, or real-world problem solving, the things that make someone great at a job. And for many candidates, especially those from underrepresented backgrounds, the format itself can feel exclusionary.
The Rise of Chat-Based Interviews
Enter conversational AI.
Organisations have been using chat-based interviews to assess talent since before 2018, and they offer a distinctly different approach.
Rather than asking candidates to rate themselves on abstract traits, they invite them into a structured, open-ended conversation. This creates space for candidates to share stories, explain their thinking, and demonstrate how they communicate and solve problems.
The format reduces stress and pressure because it feels more like messaging than testing. Candidates can be more authentic, and their responses have been proven to reveal personality traits, values, and competencies in a context that mirrors honest workplace communication.
Importantly, every candidate receives the same questions, evaluated against the same objective, explainable framework. These interviews are structured by design, evaluated by AI models like Sapia.ai’s InterviewBERT, and built on deep language analysis. That means better data, richer insights, and a process that works at scale without compromising fairness.
Key Findings from the Latest Research
The new study, published in Frontiers in Psychology, put AI-powered, chat-based interviews head-to-head with traditional psychometric assessments, and the results were striking.
One of the most significant takeaways was that candidates are less likely to “fake good” in chat interviews. The study found that AI-led conversations reduce social desirability bias, giving a more honest, unfiltered view of how people think and express themselves. That’s because, unlike multiple-choice questionnaires, chat-based assessments don’t offer obvious “right” answers – it’s on the candidate to express themselves authentically and not guess teh answer they think they would be rewarded for.
The research also confirmed what our candidate feedback has shown for years: people actually enjoy this kind of assessment. Participants rated the chat interviews as more engaging, less stressful, and more respectful of their individuality. In a hiring landscape where candidate experience is make-or-break, this matters.
And while traditional psychometric tests still show higher predictive validity in isolated lab conditions, the researchers were clear: real-world hiring decisions can’t be reduced to prediction alone. Fairness, transparency, and experience matter just as much, often more, when building trust and attracting top talent.
Sapia.ai was spotlighted in the study as a leader in this space, with our InterviewBERT model recognised for its ability to interpret candidate responses in a way that’s explainable, responsible, and grounded in science.
Why Trust and Candidate Agency Win
Today, hiring has to be about earning trust and empowering candidates to show up as their full selves, and having a voice in the process.
Traditional assessments often strip candidates of agency. They’re asked to conform, perform, and second-guess what the “right” answer might be. Chat-based interviews flip that dynamic. By inviting candidates into an open conversation, they offer something rare in hiring: autonomy. Candidates can tell their story, explain their thinking, and share how they approach real-world challenges, all in their own words.
This signals respect from the employer. It says: We trust you to show us who you are.
Hiring should be a two-way street – a long-held belief we’ve had, now backed by peer-reviewed science. The new research confirms that AI-led interviews can reduce bias, enhance fairness, and give candidates control over how they’re seen and evaluated.
Blog
AI Maturity in the Enterprise
Barb Hyman, CEO & Founder, Sapia.ai
It’s time for a new way to map progress in AI adoption, and pilots are not it.
Over the past year, I’ve been lucky enough to see inside dozens of enterprise AI programs. As a CEO, founder, and recently, judge in the inaugural Australian Financial Review AI Awards.
And here’s what struck me:
Despite the hype, we still don’t have a shared language for AI maturity in business.
Some companies are racing ahead. Others are still building slide decks. But the real issue is that even the orgs that are “doing AI” often don’t know what good looks like.
You don’t need more pilots. You need a maturity model.
The most successful AI adoption strategy does not have you buying the hottest Gen AI tool or spinning up a chatbot to solve one use case. What it should do is build organisational capability in AI ethics, AI governance, data, design, and most of all, leadership.
It’s time we introduced a real AI Maturity Model. Not a checklist. A considered progression model. Something that recognises where your organisation is today and what needs to evolve next, safely, responsibly, and strategically.
Here’s an early sketch based on what I’ve seen:
The 5 Stages of AI Maturity (for real enterprises)
- Curious
- Awareness is growing across leadership
- Experimentation led by innovation teams
- Risk is unclear, appetite is cautious
- AI is seen as “tech”
- Reactive
- Gen AI introduced via vendors or tools (e.g., copilots, agents)
- Some pilots show promise, but with limited scale or guardrails
- Data privacy and sovereignty questions begin to surface
- Risk is siloed in legal/IT
- Capable
- Clear policies on privacy, bias, and governance
- Dedicated AI leads or councils exist
- Internal use cases scale (e.g., summarisation, scoring, chat)
- LLMs integrated with guardrails, safety reviewed
- Strategic
- AI embedded in workflows, not layered on
- LLM/data infrastructure is regionally compliant
- AI outcomes measured (accuracy, equity, productivity)
- Teams restructured around AI capability — not just tech enablement
- AI-Native
- AI informs and transforms core decisions (hiring, pricing, customer service)
- Enterprise builds proprietary intelligence
- FAIR™/RAI principles deeply operationalised
- Talent, systems, and leadership are aligned around an intelligent operating model
Why this matters for enterprise leaders
AI is a capability.And like any capability, it needs time, structure, investment, and a map.
If you’re an HR leader, CIO, or enterprise buyer, and you’re trying to separate the real from the theatre, maturity thinking is your edge.
Let’s stop asking, “Who’s using AI?”
And start asking: “How mature is our AI practice and what’s the next step?”
I’m working on a more complete model now, based on what I’ve seen in Australia, the UK, and across our customer base. If you’re thinking about this too, I’d love to hear from you.
Blog
Beyond the Black Box: Why Transparency in AI Hiring Matters More Than Ever
For too long, AI in hiring has been a black box. It promises speed, fairness, and efficiency, but rarely shows its work.
That era is ending.
“AI hiring should never feel like a mystery. Transparency builds trust, and trust drives adoption.”
At Sapia.ai, we’ve always worked to provide transparency to our customers. Whether with explainable scores, understandable AI models, or by sharing ROI data regularly, it’s a founding principle on which we build all of our products.
Now, with Discover Insights, transparency is embedded into our user experience. And it’s giving TA leaders the clarity to lead with confidence.
Transparency Is the New Talent Advantage
Candidates expect fairness. Executives demand ROI. Boards want compliance. Transparency delivers all three.
Even visionary Talent Leaders can find it difficult to move beyond managing processes to driving strategy without the right data. Discover Insights changes that.
“When talent leaders can see what’s working (and why) they can stop defending their strategy and start owning it.”
Metrics That Make Transparency Real (and Actionable)
🕒 Time to Hire
What it is: The median time between application and hire.
Why it matters: This is your speedometer. A sharp view of how long hiring takes and how that varies by cohort, role, or team helps you identify delays and prove efficiency gains to leadership.
Faster time to hire = faster access to revenue-driving talent.
💬 Candidate Sentiment, Advocacy & Verbatim Feedback
What it is: Satisfaction scores, brand advocacy measures, and unfiltered candidate comments.
Why it matters: Many platforms track satisfaction. Sapia.ai’s Discover Insights takes it further, measuring whether that satisfaction translates into employer and consumer brand advocacy.
And with verbatim feedback collected at scale, talent leaders don’t have to guess how candidates feel. They can read it, learn from it, and take action.
You don’t just measure experience. You understand it in the candidates’ own words.
🔍 Drop-Off Rates, Funnel Visibility & Automation That Works
What it is: The percentage of candidates who exit the hiring process at different stages, and how to spot why.
Why it matters: Understanding drop-off points lets teams fix friction quickly. Embedding automation early in the funnel reduces recruiter workload and elevates top candidates, getting them talking to your hiring teams faster.
Assessment completion benchmarks in volume hiring range between 60–80%, but with a mobile-first, chat-based format like Sapia.ai’s, clients often exceed that.
Optimising your funnel isn’t about doing more. It’s about doing smarter, with less effort and better outcomes.
📈 Hiring Yield (Hired / Applied)
What it is: The percentage of completed applications that result in a hire.
Why it matters: This is your funnel efficiency score. A high yield means your sourcing, screening, and selection are aligned. A low one? There’s leakage, misfit, or missed opportunity.
Hiring yield signals funnel health, recruiter performance, and candidate-process fit.
🧠 AI Effectiveness: Score Distribution & Answer Originality
What it is: Insights into how candidate scores are distributed, and whether responses appear copied or AI-generated.
Why it matters: In high-volume hiring, a normal distribution of scores suggests your assessment is calibrated fairly. If it’s skewed too far left or right, it could be too hard or too easy, and that affects trust.
Add in answer originality, and you can track engagement integrity, protecting both your process and your brand.
From Metrics to Momentum
To effectively lead, you need more than simply tracking; you need insights enabling action.
When you can see how AI impacts every part of your hiring, from recruiter productivity to candidate sentiment to untapped talent, you lead with insight, not assumption. And that’s how TA earns a seat at the strategy table.