“The ATS gives it a score. Then if it's high enough someone reads it. That's what I assume.”
P19 · Business management · Q8
A closer look at how students navigate AI, the unwritten rules of recruitment, and the search for a way into work.
Explore the findings ↗GradLaunch / Phase 1 · Working report
Many sources. Different signals. A decision to make.
Participants describe catching invented qualifications, inflated responsibilities and achievements that never happened. Yet checking a claim about their own experience can be easier than judging advice about an unfamiliar hiring process. That uneven access to credible guidance connects the four themes: where recruitment beliefs come from, how advice is judged, what claims can be defended, and who is available to help.
Four themes, each with the evidence behind it and the accounts that complicate it. The working corpus spans different routes into work: graduate schemes, nursing, care management, creative work and self-employment.
Recruitment known at second hand.
Explore the theme ↗THEME / 02The difference between checking a fact and trusting advice.
Explore the theme ↗THEME / 03A future conversation becomes a present-day check.
Explore the theme ↗THEME / 04AI, human support and the cost of asking again.
Explore the theme ↗“Help me work out which jobs I can realistically do. Before it writes the application.”P16 · Business student · Q18
Four themes, with deeper analysis, participant context and accounts that complicate the argument.
Recruitment known at second hand.
Explore the theme ↗THEME / 02The difference between checking a fact and trusting advice.
Explore the theme ↗THEME / 03A future conversation becomes a present-day check.
Explore the theme ↗THEME / 04AI, human support and the cost of asking again.
Explore the theme ↗Recruitment known at second hand.
A hiring process is almost entirely invisible from the applicant's side. An application goes in; weeks later something or nothing comes back, usually without explanation. Participants describe filling that space with videos, forums, workplace experience and advice from people they know.
What separates their accounts is not confidence. Uncertainty runs through the whole corpus, and participants describing the most mechanical screening processes are often the quickest to say they are guessing. What separates the accounts is what is being described, and where it came from: whether someone is reporting a fragment of a process they watched, or a complete system they were told about.
Read the supporting evidence ↗Participants who have watched employers recruit describe the people and decisions they saw. They also say which parts of the process they did not see.
"At the foundation, managers read the forms. Two of them disagreed about a candidate while I was there. One liked the community experience and the other wasn't happy with the reporting example. They went back through it. I don't know what happened before it reached them."
— P14 · Business management · Q8
Here, two managers disagree and read the application again. P14 saw that discussion, but did not see how the application reached them.
"My placement manager read CVs and gave people a practical task. I saw that part. I didn't see what HR did before him, so I can't tell you the whole process."
— P18 · Cybersecurity · Q8
"An engineer sees it eventually. I saw the managers talking about candidates on placement. I didn't see the bit before that. Some sort of check by HR, I'd imagine."
— P02 · Mechanical engineering · Q8
"HR first, then the home manager I think. I've sat next to managers reading the forms. A small home might not have the HR bit."
— P07 · Health and care management, online · Q8
One participant reports a workplace application count that a manager showed her:
"We got 350 for reception. My manager showed me."
— P11 · Business and tourism management · Q11
Participants without that exposure describe an end-to-end pipeline. Asked where the picture came from, they name media sources rather than people or workplaces.
"ATS first. It scans the keywords, then someone in HR looks. That's what I thought anyway." … "Videos. Loads of them say it. I haven't actually seen anyone recruit, so I can't tell you for sure."
— P16 · Business management · Q8
P16 believes this because so many videos repeat it. P06 gives a more technical explanation, based on a podcast and something he saw on GitHub. Neither has seen an employer use the process they describe:
"It hits an ATS, like Workday or whatever they use. It gets scored on keyword match and criteria, and ranked. For the big firms a recruiter might only look at the top of the ranked list, so loads never get human eyes at all." … "The technology, mainly. I know what it can do, I listened to a podcast once and saw a commit on github from a guy that works there."
— P06 · Computing, online · Q8
"I imagine they check the grades and university first. Then keywords. I don't know, that's what all the advice makes you think." … "Mostly forums and videos, honestly. And being rejected. You start wondering what they didn't like when they don't tell you."
— P17 · Law · Q8
When employers give no reason for a rejection, P17 turns to online advice to try to understand what went wrong.
"They probably test people first. There are too many CVs to read. Some bot must sort them out, I'd think."
— P09 · Business and computing · Q8
The account students absorb describes a large-employer graduate scheme. Many participants are not entering one. The accounts suggest a tension between a generic screening model and the different recruitment routes participants describe. They do not establish what software any employer uses.
"I think the ward manager or recruitment team reads the form and scores it against the person spec. That's what we've been told on placement."
"It doesn't really feel like a graduate job search to me. More like finishing placement and qualifying."
— P05 · Adult nursing · Q8, Q19
"A lot of it is who knows you. The broadcaster schemes have forms and tests, but I've seen other jobs get filled before anyone even puts them online." … "Somebody knows somebody and the job's gone. If you don't know anyone yet, where do you start?"
— P12 · Film, TV and digital production · Q8
"At a small brand it could just be the founder opening your email and looking at the portfolio. That's closer to what I saw on the internship."
— P10 · Fashion marketing · Q8
"A hundred? For the big-company one. Care's different, we struggle to get people."
— P07 · Health and care management, online · Q11
A labour-shortage sector, described by someone working in it, while the surrounding discourse describes scarcity.
"The graduate schemes tell you the stages. Forms, tests, interviews, assessment centre. I've been through some of those. At work we use an agency and the manager interviews people. I don't think there's one process for all of them."
— P20 · Business management · Q8
That last account explicitly distinguishes recruitment routes and resists generalising from one to all others.
Videos, job pages, other people and workplace experience provide different kinds of knowledge. Accounts of the source matter as much as the belief itself.
An assumed ranking system can make keywords feel decisive. A placement can make interviews and practical tasks more visible. Neither necessarily reveals the whole process.
What students think employers look for can shape what they change. Someone who expects keyword screening may add keywords to their CV.
Uncertainty is not confined to one group. Participants describing automated screening frequently qualify it: "that's what I assume", "that's what I thought anyway", "I don't know, that's what all the advice makes you think". An earlier version of this analysis claimed that confidence runs inversely to exposure. The interviews do not support that and the claim was dropped. What differs is the shape of the account and the source it is traced to, not how certain anyone sounds.
Technical capability does not transfer. P06 verifies technical claims rigorously, works in IT and has experimented with the API to batch-tailor applications. His recruitment model is still sourced from a podcast. Technical fluency alone does not appear to explain this account of recruitment.
Exposure is not binary. P03 has recruited hourly staff in the shop she manages and describes that process accurately, while saying plainly that head office recruitment is "different. I haven't seen that side." Partial visibility is the normal condition, not an edge case.
Selected quotations. Participant codes link to the profile index; question numbers locate each extract in the transcripts.
“The ATS gives it a score. Then if it's high enough someone reads it. That's what I assume.”
P19 · Business management · Q8
“Videos. Loads of them say it. I haven't actually seen anyone recruit, so I can't tell you for sure.”
P16 · Business management · Q8
“Not that certain, actually. I know they use the systems. I don't know the settings. I still put the keywords in because well at least that's something I can do.”
P03 · Business and marketing, online · Q8
“At the foundation, managers read the forms. Two of them disagreed about a candidate while I was there. One liked the community experience and the other wasn't happy with the reporting example. They went back through it. I don't know what happened before it reached them.”
P14 · Business management · Q8
“My placement manager read CVs and gave people a practical task. I saw that part. I didn't see what HR did before him, so I can't tell you the whole process.”
P18 · Cybersecurity · Q8
“The graduate schemes tell you the stages. Forms, tests, interviews, assessment centre. I've been through some of those. At work we use an agency and the manager interviews people. I don't think there's one process for all of them.”
P20 · Business management · Q8
“It doesn't really feel like a graduate job search to me. More like finishing placement and qualifying. Sorry if that hasn't been much help.”
P05 · Adult nursing · Q19
“A lot of it is who knows you. The broadcaster schemes have forms and tests, but I've seen other jobs get filled before anyone even puts them online.”
P12 · Film, TV and digital production · Q8
Quotations retain transcript wording; omissions within sentences are marked. Methods and source notes ↗
The difference between checking a fact and trusting advice.
Participants describe several ways of checking AI output, with different strengths and limits. They recognise an invented responsibility, test a technical claim against documentation, ask a mentor, open the company's own website. This is not a corpus of people accepting whatever they are given.
But the methods reach unevenly. Participants can often compare a claim with their own experience, although their accounts also describe missed errors. Checking whether a piece of advice will help an application requires knowing how the application will be assessed — and access to that knowledge varies across the accounts. The problem is not an absence of scrutiny. It is the uneven availability of something credible to check against.
Read the supporting evidence ↗Participants describe several bases for judgment, ranging from external references to familiarity with their own experience and craft.
Against their own experience. Participants use what they know about their work to spot invented achievements and check technical claims.
"If it's made up something about my job, I'll know. I check it against my figures and what they've asked for."
— P03 · Business and marketing, online · Q12
"The technical stuff I can check. If something looks odd, I'll look at the docs or try it myself."
— P06 · Computing, online · Q12
"It got an event ID wrong, so I went and checked that. Still useful practice."
— P18 · Cybersecurity · Q3
Against how they actually speak.
"For language, I can judge it cuz I couldn’t write like that. I also read the output aloud to see if it sounds like something I could actually say."
— P08 · Criminology and law, online · Q12
Against craft judgement. One participant describes a route the others do not have — she can see exactly what a tool changed.
"With the editing software I can see what a feature has changed. It's easier to judge that than a whole generated idea."
— P12 · Film, TV and digital production · Q4
One participant combines several forms of checking:
"Company information, their website. Technical things, course notes or someone qualified. With wording I check that my manager would recognise the job I'm describing. If it makes me responsible for everything, something's gone wrong."
— P20 · Business management · Q12
The same participants describe a hard stop when the claim concerns recruitment itself.
"The recruitment stuff is harder. I don't know what settings they've got."
— P06 · Computing, online · Q12
"Technical stuff, test it or check the documentation. Recruitment advice is harder. I ask people who've hired analysts. I don't put much weight on a CV score."
— P18 · Cybersecurity · Q12
"When it starts telling me about ATS scores, I don't know how to check that."
— P03 · Business and marketing, online · Q12
"I don't really know. If it sounds generic I don't like it."
— P04 · History · Q12
"If you get the job maybe? I’m not sure."
— P11 · Business and tourism management · Q12
That last answer is worth sitting with. The only test she can imagine is the outcome — which arrives long after the decision it was meant to inform, if it arrives at all.
When an external standard is difficult to access, participants describe other signals that feel informative. An opaque score, institutional endorsement, agreement between tools and AI reviewing its own output raise different questions about the basis for trust.
A score.
"I like seeing the score go up. It makes me feel I've improved it, even if I can't really tell from reading it."
— P19 · Business management · Q4
Institutional branding.
"The university logo made it feel approved I guess so I treated the score more seriously. I don't know whether it uses the same sort of model as everything else."
— P09 · Business and computing · Q12
Agreement between tools.
"I compare the answers. If two tools say the same thing, I tend to believe it more. I know that might not mean much."
— P16 · Business management · Q12
Asking the machine to review itself.
"I know I'm asking the same thing that wrote it, but I still do it."
— P19 · Business management · Q12
The awareness is not the missing piece. Some participants recognise the weakness of a signal while continuing to find it reassuring. Alternatives may exist but feel less accessible or immediate.
Keyword insertion continues alongside expressed scepticism; it is described as something concrete the participant can do.
"Not that certain, actually. I know they use the systems. I don't know the settings. I still put the keywords in because well at least that's something I can do."
— P03 · Business and marketing, online · Q8
"Matching what they ask for. Experience is probably the main thing …" … "I still think the keywords matter."
— P16 · Business management · Q9
Without a standard, there is no signal that a draft is finished. Several participants describe revision expanding to fill the time available.
"I couldn't choose. One gave it a better score than the other and I was trying to work out why. It was only about three hundred words. I spent most of the evening on it."
— P17 · Law · Q3
"Can it make me stop editing? That's what I'd want. Give me a couple of versions and make me choose. I'd probably open another chat, actually, so I don't know if that would work."
— P17 · Law · Q18
"Like how do you know when enough is enough cuz you can really spiral outta control. I can spend hours trying different versions until I no longer know which one is clearer."
— P09 · Business and computing · Q14
"Bits of it. Then I asked it to compare the two versions. By the end it was close to what I'd written in the first place. I do that quite a lot."
— P04 · History · Q3
"I'll feel better for ten minutes, then it gives me five alternatives and I wonder what was wrong with the first one."
— P01 · Psychology · Q19
In this account, a deadline or another person provides a stopping point:
"The deadline. Or my sister saying enough now. Usually the deadline."
— P17 · Law · Q14
A draft or suggestion provides something concrete to work with. The immediate benefit may be speed, structure or language.
A score or another AI judgment offers a visible response. Agreement is not necessarily independent evidence of quality.
Without a clear standard, another version can reopen the decision. Some accounts describe deadlines or people interrupting this loop; not everyone enters it.
Checking a fact about yourself is not a minor thing. Removing an invented qualification prevents a participant walking into an interview unable to defend their own CV. Several participants describe exactly that catch. These checks catch real mistakes. They just cannot tell students whether the rest of the advice will help them get a job.
Some participants have an external standard and use it. P05 checks against the NHS person specification. P20 checks against what his manager would recognise. P02 and P14 ask people who have hired in their field. A written specification or knowledgeable person can provide a more concrete basis for judgment without resolving every uncertainty.
Not everyone enters the revision loop. P15 describes leaving an application for another day rather than iterating into the night. P20 says CV editing "doesn't take that long now" and has reallocated his effort to timed tests. The loop is a pattern in the corpus, not a universal experience.
Selected quotations. Participant codes link to the profile index; question numbers locate each extract in the transcripts.
“When it doesn't sound like me. I'll ask ChatGPT which version is best as well, which probably defeats the point. It always has a reason for its choice, though, and I find that reassuring.”
P17 · Law · Q12
“The technical stuff I can check. If something looks odd, I'll look at the docs or try it myself. The recruitment stuff is harder. I don't know what settings they've got.”
P06 · Computing, online · Q12
“If it's made up something about my job, I'll know. I check it against my figures and what they've asked for. When it starts telling me about ATS scores, I don't know how to check that.”
P03 · Business and marketing, online · Q12
“Maybe ease of use? The university logo made it feel approved I guess so I treated the score more seriously. I don't know whether it uses the same sort of model as everything else.”
P09 · Business and computing · Q12
“ChatGPT every day. Canva, Grammarly. Sometimes Gemini. And the CV checker. I like seeing the score go up. It makes me feel I've improved it, even if I can't really tell from reading it.”
P19 · Business management · Q4
“I couldn't choose. One gave it a better score than the other and I was trying to work out why. It was only about three hundred words. I spent most of the evening on it.”
P17 · Law · Q3
“I'd ask the placement team if the CV was any good. Or my friend who's an engineer. AI's fine for a sentence.”
P02 · Mechanical engineering · Q12
“Company information, their website. Technical things, course notes or someone qualified. With wording I check that my manager would recognise the job I'm describing. If it makes me responsible for everything, something's gone wrong.”
P20 · Business management · Q12
“It's there if I need to get started. I don't use it instead of an appointment, really. If anything I'm more likely to leave the application for another day than spend all night talking to ChatGPT.”
P15 · Business management · Q17
Quotations retain transcript wording; omissions within sentences are marked. Methods and source notes ↗
A future conversation becomes a present-day check.
Before keeping an AI-written claim, participants picture an interviewer asking them to explain it. Could they give a real example?
That question helps them spot exaggerations. They remove claims they would struggle to explain because they do not want to be caught out in an interview. Fear of that awkward moment keeps the application closer to the truth—though it does not catch every false claim.
Read the supporting evidence ↗The same reasoning appears across completely different fields, in participants' own words.
"Not when I picture the interview. They'll ask what I actually did, and then I'm explaining why the CV doesn't mean what it says."
— P18 · Cybersecurity · Q13
"It once added a load of skills to my CV I'd never claimed, like being proficient in data analysis software, and I deleted them because I'd have been caught out instantly in an interview."
— P01 · Psychology · Q13
"It'll make it sound like I transformed a society when I was just a member. I take that out. If they asked me about it, I wouldn't have anything to say."
— P04 · History · Q13
"They might. I'd be more worried about not being able to explain the answer. If they ask about my work, I need to know exactly what I've claimed."
— P20 · Business management · Q15
That answer was given to a question about whether employers can detect AI writing. He ranks defensibility above detection without being prompted to.
The catches themselves are precise:
"It said I advised senior stakeholders on regulatory risk. I help in a shop."
— P17 · Law · Q13
"It called a phishing exercise red-team experience. And monitoring became threat hunting. They sound close if you don't do the work, but they're different things."
— P18 · Cybersecurity · Q13
"It also tried to turn attendance going up into revenue, which we hadn't measured."
— P14 · Business management · Q13
"It also called the documentary award-winning which was hilarious cuz it only screened at our course showcase …"
— P12 · Film, TV and digital production · Q3
"it made my supporting statement sound really formal and business-y, I rejected most of it. It didn't sound like a caring nurse, it sounded like a manager."
— P05 · Adult nursing · Q13
"It suggested chartered accountant for my headline, as an ambition. I haven't qualified. I kept student and accounts assistant. I don't want someone reading it quickly and getting the wrong idea."
— P20 · Business management · Q13
Participants identify three conditions under which it fails, and they name them themselves.
Tiredness.
"When I'm exhausted I don't check as well. I know I should. Sometimes I just want it sent."
— P11 · Business and tourism management · Q14
Volume.
"Definitely. The first one gets checked properly. By the last one I'm just trying to get it sent."
— P19 · Business management · Q2
Familiar vocabulary. The subtlest of the three, and the one participants are least able to guard against:
"Not straight away. It was using words I recognised, so I read past it. Then I thought about them asking me how the system works and realised I'd made it sound like my responsibility."
— P16 · Business management · Q3
"I took it out, but some of the smaller stuff is harder to notice."
— P16 · Business management · Q13
And when it fails, nothing tells anyone:
"One application said I managed a regional promotion. I managed it in my store and sent feedback to the region. I noticed after submitting. I didn't get an interview, so nothing happened but I’d hate to be caught out on these types of lies I’d be mortified."
— P03 · Business and marketing, online · Q13
One exchange shows a stated practice giving way under a specific question:
"I think so. I'd have to open it. There are so many versions. I normally say I check the numbers, but now you're asking about a specific one I'm less sure."
— P19 · Business management · Q13
Some participants use AI to improve their written English. They worry that an interviewer will hear them speak differently and mistake that help for dishonesty.
"I worry that the application will sound more fluent than I am when speaking, and the interviewer will think I was dishonest. I try to keep the application to be clear, not to pretend that English is my first language."
— P08 · Criminology and law, online · Q14
P08 holds back on polishing his English because he does not want an interviewer to think he lied.
"When they meet you and your English is different, probably. But people get help with English anyway. I don't think that should mean you can't have the job."
— P11 · Business and tourism management · Q15
"I need the steps first. A big block of text is difficult with dyslexia. So I get it to pull out what they're asking for, then do one bit of my CV at a time. I listen to the changes as well."
— P09 · Business and computing · Q5
"And when it changes my English, explain why. Otherwise I just copy the sentence."
— P08 · Criminology and law, online · Q18
P08 wants to understand the changes so he can learn from them, rather than just copy the new sentence.
Accounts from participants with professional experience connect confidentiality principles to AI use, and describe recognising risks beyond simply removing a name.
"I never paste names, details of residents or an incident report into it they really drilled that into us."
— P07 · Health and care management, online · Q14
"Confidentiality. Taking a name out doesn't always make the story anonymous. I nearly typed a work example in once and then thought, no, there's too much in that."
— P15 · Business management · Q14
"I use made-up events for practice, not client logs."
— P18 · Cybersecurity · Q14
"I take the private details out first. It's not worth putting somebody's information in there for a better paragraph."
— P05 · Adult nursing · Q14
These accounts suggest a principle carrying across contexts. They arose in response to questions about concerns and risks; they do not establish that every participant learned it in the same way.
Honesty is a by-product. The question is: “Could I defend this in an interview?” That can lead students to remove false claims. But it can also leave room for exaggerations they think they could explain. Avoiding embarrassment comes first; a more honest application follows.
No feedback means no chance to learn. If an employer rejects an application without explaining why, the student cannot tell whether an exaggerated claim caused the problem.
Help with writing does not mean someone has lied. AI can help students express real experience in clearer English. If an employer treats polished writing as suspicious, students who need language or accessibility support may feel they have to hold back.
Selected quotations. Participant codes link to the profile index; question numbers locate each extract in the transcripts.
“Some of the wording. If it says I managed suppliers, I take that out. Buying a small amount of fabric isn't really managing a supplier, is it?”
P13 · Business and entrepreneurship · Q5
“Not straight away. It was using words I recognised, so I read past it. Then I thought about them asking me how the system works and realised I'd made it sound like my responsibility.”
P16 · Business management · Q3
“Not when I picture the interview. They'll ask what I actually did, and then I'm explaining why the CV doesn't mean what it says.”
P18 · Cybersecurity · Q13
“Definitely. The first one gets checked properly. By the last one I'm just trying to get it sent.”
P19 · Business management · Q2
“When I'm exhausted I don't check as well. I know I should. Sometimes I just want it sent.”
P11 · Business and tourism management · Q14
“Not a complete invention, but yes. One application said I managed a regional promotion. I managed it in my store and sent feedback to the region. I noticed after submitting. I didn't get an interview, so nothing happened but I’d hate to be caught out on these types of lies I’d be mortified.”
P03 · Business and marketing, online · Q13
“I worry that the application will sound more fluent than I am when speaking, and the interviewer will think I was dishonest. I try to keep the application to be clear, not to pretend that English is my first language.”
P08 · Criminology and law, online · Q14
“Confidentiality. Taking a name out doesn't always make the story anonymous. I nearly typed a work example in once and then thought, no, there's too much in that. I deleted it before sending.”
P15 · Business management · Q14
“I need the steps first. A big block of text is difficult with dyslexia. So I get it to pull out what they're asking for, then do one bit of my CV at a time. I listen to the changes as well.”
P09 · Business and computing · Q5
Quotations retain transcript wording; omissions within sentences are marked. Methods and source notes ↗
AI, human support and the cost of asking again.
Participants describe AI as convenient, patient and free of the embarrassment that comes with asking a person a basic question. Those qualities make it attractive even to participants who doubt the quality of what it tells them.
Some participants have a placement manager, mentor or colleague they can ask for advice. Others turn to AI because they struggle to find someone to help. Who is available matters, though it does not explain every choice.
Read the supporting evidence ↗"I ask AI first now. I used to ask people more. It's easier when it's late and I don't want to bother anyone. Sometimes I think I use that as an excuse, though."
— P17 · Law · Q17
She establishes the absence earlier in the same interview: "I haven't got anyone in the family who's done law, so there's a lot I'm trying to find out."
"Most of the writing goes through it now. I used to ask friends more. You can ask ChatGPT ten times and it doesn't get annoyed, which is probably why I don't stop."
— P19 · Business management · Q17
Set against participants with people to ask:
"I'd ask the placement team if the CV was any good. Or my friend who's an engineer. AI's fine for a sentence."
— P02 · Mechanical engineering · Q12
"If it points to something I've missed, I can check that. The general advice, I'm less sure. I'll send it to my old supervisor if it's important. She doesn't write much back, but it's usually useful."
— P14 · Business management · Q12
"The person spec, mostly. And I'd ask my mentor. I trust her on what I should say about placement."
— P05 · Adult nursing · Q12
"I use it to get ready. The contacts are still where I hear about jobs."
— P10 · Fashion marketing · Q17
Alongside practical access and perceived relevance, participants describe a social cost to asking for help.
"Partly timing and partly embarrassment, if I'm honest. I feel I should arrive with a finished plan."
— P01 · Psychology · Q16
"I don't really want to explain how many applications I've sent." … "They might say I've been doing it wrong. Which could be useful, I know. I still don't want to hear it."
— P19 · Business management · Q16
She names the feedback as valuable and avoids it anyway.
"The appointment was good. She suggested local firms I hadn't been looking at and got me to talk through the shop example. I should book again. It feels awkward coming back with the same problems."
— P17 · Law · Q16
"It's my first place for questions now. You can ask something really basic and it doesn't matter. I find it harder to ask a lecturer when I feel I should already know the answer."
— P16 · Business management · Q17
"It feels close enough for remembering examples but not for pressure or judgement. A person is obviously better but realistically you’re not gonna have someone with enough patience for you to rehearse these things."
— P12 · Film, TV and digital production · Q6
In these accounts, patience and the absence of judgment are advantages even where confidence in advice quality is limited.
Where participants describe careers support working, it did something specific: it changed how they understood the problem, rather than improving the text.
"She asked if the jobs would actually get me where I wanted. I'd been asking ChatGPT to fix the applications, not really thinking about that."
— P03 · Business and marketing, online · Q16
"The recording showed I wasn't listening when someone raised a risk. I acknowledged it and carried on with my list. I hadn't noticed myself doing that." … "I didn't spot the listening thing myself, and an AI-written answer about teamwork wouldn't have shown me that."
— P20 · Business management · Q6, Q17
"There was a shot I really liked, but another student designed and lit it. I was assisting. The tutor asked what it showed about my work. I didn't like taking it out, but I could see why."
— P12 · Film, TV and digital production · Q16
"I had a list of duties. At the workshop they got me to use the night handover instead. We'd had the same problem coming up and I got people to use a clearer checklist. I hadn't thought of putting that on the CV."
— P11 · Business and tourism management · Q16
"Stop writing like a supporter. Explain what I could do for them. That was before the placement application, and I still catch myself doing it."
— P14 · Business management · Q16
"She showed us CVs from people who'd been hired. And what the job was actually like, chasing samples, changing product copy. That was useful."
— P10 · Fashion marketing · Q16
These examples include reframing, noticing behaviour and selecting relevant experience, rather than wording alone. Some participants contrast that experience with an AI tool’s tendency to agree:
"ChatGPT tends to go along with whatever I say I want to do there’s never any pushback which is not great."
— P03 · Business and marketing, online · Q17
"Sometimes I feel the AI is too positive and is only trying to fulfill my request to what it considers to be the best result …"
— P21 · Business management · Q13
Asked to design ideal support, participants split. One group asks for judgement.
"Help me work out which jobs I can realistically do. Before it writes the application. I can get a nice statement already, that's not the problem."
— P16 · Business management · Q18
"I'd want it to tell me if something's bad. Not just say I'm great. And then actually help me fix it."
— P01 · Psychology · Q18
"I'd like it to tell me if I've got a chance. I know it can't really know that. But something more helpful than a higher score and then another rejection."
— P19 · Business management · Q18
"Better technical practice, probably. Show me a problem and let me work through it. I'd want to see why it marked an answer wrong. I don't need it writing another CV."
— P18 · Cybersecurity · Q18
"I'd like to speak to someone doing one of these assistant jobs. Just ask what their day is like. The descriptions don't help me picture it very well."
— P13 · Business and entrepreneurship · Q18
Another group asks for something different entirely — access, timing, and support that fits a life.
"Evening appointments would help. They don't all have to be face to face. I could talk through a form after I've finished everything else. I don't need a new app for that, I don't think."
— P15 · Business management · Q18
"Audio, and simpler text. I'd like to practise with the video timer too. That's the bit the chat doesn't help with."
— P09 · Business and computing · Q18
"It would have to take my shifts and my daughter into account. And the care qualifications. I wouldn't want to keep explaining all that every time."
— P07 · Health and care management, online · Q18
"I'd want to put in the job and find out what I need for it. I'm looking at prisons and probation and it's not all the same. And when it changes my English, explain why."
— P08 · Criminology and law, online · Q18
These are not versions of the same request. A design answering only the first group leaves the second where it already is.
A difficult choice may concern direction, eligibility, access or confidence rather than the wording of an application.
Is anyone available? Will they judge me for asking a basic question—or asking it again? These worries can make AI easier to turn to.
AI may be immediately available; people may offer situated judgment. The design question is how to connect useful forms of support rather than assuming one replaces the other.
Limited engagement with careers services cannot be reduced to embarrassment. Timing, shift patterns, caring responsibilities, commuting, and a judgement that the service is not relevant to a particular sector all appear in the accounts. P06 dismissed his university's provision as out of date rather than intimidating. P05 and P07 go to occupational mentors because that is where the relevant expertise sits, not because a careers adviser would make them uncomfortable.
Wanting judgement is not universal. A substantial group described access needs instead — evening availability, audio and accessibility features, scheduling that accommodates shift work and childcare, explanations rather than output. Reading those as votes for a more opinionated tool would misrepresent them.
One participant rejects the frame entirely. P21 is building a freelance business and is not applying for graduate roles: "I'm dissapointed in how the job market operates and I really want nothing to do with it. I only want to make it by myself." He does not say what produced that view and was not asked, so nothing should be inferred about its cause. He is also explicit about disclosing AI use while questioning its output — "It will lie to you if you let it." A support model built around employment applications does not describe what he needs.
Selected quotations. Participant codes link to the profile index; question numbers locate each extract in the transcripts.
“Nobody in my family really knows about graduate jobs. I think that's why I ask it so much. I don't have someone at home who's been through it.”
P16 · Business management · Q19
“Most of the writing goes through it now. I used to ask friends more. You can ask ChatGPT ten times and it doesn't get annoyed, which is probably why I don't stop.”
P19 · Business management · Q17
“Partly timing and partly embarrassment, if I'm honest. I feel I should arrive with a finished plan. I am struggling a bit to decide on a specific direction and I feel like if I don’t do it very specifically then I can’t really achieve anything.”
P01 · Psychology · Q16
“I ask AI first now. I used to ask people more. It's easier when it's late and I don't want to bother anyone. Sometimes I think I use that as an excuse, though.”
P17 · Law · Q17
“She asked if the jobs would actually get me where I wanted. I'd been asking ChatGPT to fix the applications, not really thinking about that.”
P03 · Business and marketing, online · Q16
“It helps with writing when I need it. I'd still want someone to watch the interview or group practice. I didn't spot the listening thing myself, and an AI-written answer about teamwork wouldn't have shown me that.”
P20 · Business management · Q17
“Evening appointments would help. They don't all have to be face to face. I could talk through a form after I've finished everything else. I don't need a new app for that, I don't think.”
P15 · Business management · Q18
“Audio, and simpler text. I'd like to practise with the video timer too. That's the bit the chat doesn't help with.”
P09 · Business and computing · Q18
“I'd ask the placement team if the CV was any good. Or my friend who's an engineer. AI's fine for a sentence.”
P02 · Mechanical engineering · Q12
“I am slowly growing my own business in digital marketing, I already found a couple of small businesses I am working with and offering marketing services and still looking for more business.”
P21 · Business management · Q2
Quotations retain transcript wording; omissions within sentences are marked. Methods and source notes ↗
A difficult job market is one thing. Knowing how to navigate it is another. Numbers need a denominator; advice needs a context.
ISE reports an 8% fall in graduate vacancies in its surveyed market. This is evidence of pressure among participating employers, not a census of every UK employer.
Read the ISE source ↗ISE reports an average of 140 applications per graduate vacancy. Applicants can apply for multiple jobs; this figure cannot tell us how many distinct graduates are competing across the market.
Read the ISE source ↗The working analysis examines beliefs about automatic ranking and keyword thresholds. An account of that belief is not proof of an employer’s actual configuration—and observing a later human stage does not rule out earlier automation.
Explore recruitment beliefs ↗Selected media examples can help frame an interpretation. They do not establish which content someone encountered, whether it changed their beliefs, or whether it affected application quality. The proposed information-environment analysis remains a working direction, rather than a completed systematic study of media.
What should an intervention help someone understand, decide or do? These are design hypotheses to test—not outcomes already demonstrated.
Help users distinguish the recruitment stages relevant to their target roles. Link explanations to employer-specific or otherwise credible sources.
Test whether users can identify the next stage and explain why a preparation task matters. Do not imply access to an employer’s private screening rules.
Separate checks of truthfulness, relevance and clarity. Give reasons and references where possible rather than presenting a single score as proof of quality.
Compare whether explanations support better decisions, not just greater confidence or higher satisfaction.
Offer a bounded review process with explicit checks and a clear handoff to action or human support.
Test whether stopping guidance reduces unproductive revision without overlooking important errors. Some users may not need it.
Ask for the concrete experience behind a sentence. Check that AI has not inflated responsibility, invented numbers or changed what happened.
Test the defensibility of claims under realistic workload. Preserve legitimate language assistance and accessibility support.
Provide routes to human advice, practical preparation and support that fits work schedules and career goals.
Establish which needs software can meet and which require changes to access, provision or human support.
Scope, method and interpretation behind this working report.
GradLaunch is a doctoral research project at Chester Business School examining whether structured, bounded use of generative AI can improve graduate employability outcomes. It runs in two phases: an exploratory qualitative phase, reported here, and a randomised controlled trial testing an AI-supported employability intervention against standard careers resources.
Phase 1 asks how final-year and final-stage undergraduates actually use generative AI in job-seeking, what they understand about how recruitment works, and what support they are missing. Its findings inform the design of the Phase 2 intervention.
Twenty-one semi-structured interviews were conducted in August 2026 with final-year and final-stage undergraduates across thirteen UK institutions.
Each interview followed a twenty-question topic guide covering current job-search activity, AI use across CV work, interview preparation and LinkedIn, beliefs about recruitment processes, how AI output is judged, concerns and perceived risks, and experience of university careers support. The topic-guide outline is published below.
The corpus comprises 23,687 words of dialogue, of which 15,605 words are participant speech.
Participants ranged in age from 21 to 38 and included full-time campus students, online and evening learners, and mature students studying alongside full-time work. Disciplines spanned business and management, engineering, nursing, computing, law, criminology, psychology, history, cybersecurity, health and care management, fashion marketing, and film and television production. Intended destinations included graduate schemes, NHS Band 5 nursing posts, care management, freelance and self-employed routes, and short-contract creative work.
Participants were recruited through public posts on LinkedIn announcing the research and inviting final-year and final-stage undergraduates to take part. In return for an interview, the researcher offered to answer any questions participants had about AI and employability. Participation was entirely self-selecting; no participant was approached directly and no institution circulated the invitation on the researcher's behalf.
Ethical approval was granted by the Chester Business School Research Ethics Committee on 29 May 2026.
All participants received a participant information document and gave written informed consent before taking part. The consent obtained covers publication of verbatim quotations in anonymised form, which is how every quotation on this site is presented.
Participants are identified by code throughout. Names used during the interviews have been replaced, and identifying references to employers, colleagues and specific workplaces have been removed or generalised.
In return for taking part, participants were offered answers to their own questions about AI and employability. This reciprocal arrangement is disclosed because it forms part of how the sample was constituted: those who responded were, by definition, people who wanted that conversation.
Phase 1 addresses four questions:
The analysis uses reflexive thematic analysis (Braun & Clarke, 2006; 2019). Transcripts were coded in NVivo against a codebook of twenty-five codes organised under four candidate themes, with coding revisited as the themes developed. Analytic memos were written throughout and form part of the audit trail.
Theme significance is not inferred from frequency. Reflexive thematic analysis does not treat the number of participants mentioning something as a measure of its importance, so no counts or percentages are reported. Where prevalence is described it is described in words, and qualifiers such as "most" or "all" are avoided unless the full corpus supports them.
Contrasting and disconfirming accounts are presented alongside supporting evidence in each theme rather than relegated to a limitations note. Where the data complicates an interpretation, that is shown.
The researcher conducting this study is also developing GradLaunch, the intervention Phase 2 will test. That interest can shape what appears significant in qualitative material, and it is stated here rather than left implicit.
Two safeguards are applied. First, accounts that complicate or contradict the emerging interpretation are actively sought and reported — the "where the story gets less tidy" passage in each theme exists for this purpose. Second, the design responses in this report are framed as hypotheses requiring testing, not as requirements the findings have established. Phase 1 can say what an intervention should respond to. It cannot establish that a particular intervention works.
Generative AI was used in this analysis and the use is disclosed in full.
AI assistance was used to produce a first coding pass over the corpus, to locate candidate extracts, to draft the codebook structure, and in preparing and editing this report. Theme development, the interpretation of the material, decisions about what the evidence supports, and all final judgements are the researcher's own. A first-pass coding frame produced with AI assistance is a starting point for analysis, not a substitute for it, and the coding was revisited independently.
No AI-assisted analysis features were used inside the qualitative analysis software. NVivo's AI Assistant is disabled at institutional level and was unavailable throughout; all coding within NVivo was carried out manually by the researcher.
No participant data was processed using tools outside those described in the approved ethics application.
Quotations reproduce the interview transcript wording. Selected excerpts may omit surrounding text; cuts within a sentence are marked. Each quotation carries a participant code and the question number it answers, so any extract can be traced back to its position in the corpus.
Headings, framing sentences and the notes beneath each quotation are editorial interpretation, not participant speech.
Diagrams are conceptual. Their arrows indicate an interpretive relationship or a possible sequence, not a measured effect. The overview illustration maps the sources of advice available to a job-seeking student; it does not represent how often each is used or how strongly each influences decisions.
The sample is not representative of UK final-year undergraduates. It leans toward mature students, students studying online or in the evening, students working alongside their degree, and widening-participation routes. This shaped what the study could see: the prominence of insider access and of practical constraints on getting help follows partly from who took part. A campus-based, full-time, traditional-age sample would likely produce a different emphasis.
Recruitment through LinkedIn shaped the sample twice over. Participants had to be active enough on the platform to encounter the invitation, and motivated enough by the offer of AI and employability guidance to respond. Both filters point in the same direction: toward students already engaged with AI and already thinking about the transition into work. Students who use neither AI nor LinkedIn are absent by construction.
This carries a further consequence that should be stated plainly. LinkedIn is not only the recruitment channel but also one of the information sources participants describe drawing on when forming their picture of how recruitment works. The study reached participants through a channel that is itself part of what the study examines. That does not invalidate the accounts, but it means the corpus is likely to over-represent students immersed in online careers discourse rather than students insulated from it.
These are accounts, not observations. Participants describe what they do and believe. Self-report is subject to recall, to rationalisation after the fact, and to what people are willing to tell an interviewer. Where a participant's stated practice appeared to shift under closer questioning, that is shown rather than smoothed over.
Beliefs about recruitment are not evidence about recruitment. Nothing here establishes how any employer actually screens applications. A participant describing automated ranking is evidence of a belief, not of a process.
No causal claims are made. The relationships described — between exposure and understanding, between available information and belief, between belief and behaviour — are interpretive. This design cannot establish that one produced another.
Preferences are not effects. Participants describing what they would want from a support tool tells us about felt needs. It does not tell us that such a tool would improve their outcomes. That is what Phase 2 is for.
All interviews were conducted by a single researcher. One interviewer means one interviewing style, one set of instincts about which answers to probe further, and one relationship with each participant. Follow-up questions were not standardised, so some accounts were pressed harder than others. Where a participant's stated practice shifted under closer questioning, that shift is visible in the quoted material rather than hidden.
The analysis was also carried out by that same researcher. In reflexive thematic analysis this is not treated as a deficiency to be corrected: Braun and Clarke are explicit that themes are generated through the researcher's engagement with the data rather than discovered independently of it, and that inter-rater reliability measures are conceptually inconsistent with the method. The relevant safeguard is not a second coder but transparency about position and interpretation, which is what the researcher-position statement above is for.
The analysis is in progress. Themes presented here are candidates. Continued engagement with the corpus may revise, merge or discard them.
Transcript verification remains incomplete. The website uses the earlier edited transcript files. Their wording has not been checked against original interview records.
The twenty topics covered by the guide:
Follow-up probes varied by participant and are visible in the quoted material.
Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101.
Braun, V. & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597.
Institute of Student Employers. Student Recruitment Survey 2025.
Phase 1 Findings · Working research report · September 2026
A guide to the 21 participant profiles in the working report. Names and institutions are omitted from individual entries.
The profile descriptions are those reported in the working copy. They are not a representative sample description or evidence of coverage by themselves. Some profile values were changed in the earlier corpus; their reconciliation with original records remains part of the version-history issue described in About the study.
The range of routes into work helps contextualise the accounts. The index does not demonstrate how comprehensively each interview was analysed, and the sample is not representative of UK undergraduates.