Artificial intelligence is now passing the roadside interview: a UK recycling firm’s hands‑on comparison of three mainstream chatbots found surprisingly competent guidance on common car faults, while also underscoring clear boundaries where professional inspection and human judgement remain essential.
Background / Overview
The testing was commissioned by Scrap Car Comparison (SCC), described in reporting as the UK’s largest vehicle‑recycling comparator, and evaluated the outputs of OpenAI’s ChatGPT, Google AI Overview, and Microsoft Copilot against practical, real‑world vehicle problems supplied as owner scenarios. Responses from each system were graded on safety, legality, accuracy, and usefulness by Tim Singer, a workshop manager at BMS Cars who reviewed the answers and provided the practical, mechanic‑level perspective used to assess each chatbot’s quality.The experiment’s central claim — that conversational AI can deliver responsible, usable advice for routine automotive questions — is not framed as a replacement for traditional diagnostics, but as an accessible first step for motorists who want triage, cost/benefit context, or safe DIY boundaries before heading to a garage. Reported coverage of the study highlights both the strengths of modern large language model (LLM) assistants and the attendant risks of over‑reliance or misapplied DIY advice.
How the study was run (methodology and context)
The SCC study asked each AI tool a series of owner‑style prompts rather than technician‑only queries — the sorts of questions a non‑expert would type into a phone at the side of the road. Examples included multi‑fault scenarios (a high‑mileage 2010 Renault Clio with a slipping clutch, failed air conditioning, and a failed safety inspection) and single‑issue prompts such as whether an engine warning light merits paying for a diagnostic or whether it’s sensible to scrap an old vehicle. Responses were scored for four dimensions: safety, legality, accuracy and usefulness — a practical rubric aimed at the end user rather than academic metrics.Using owner‑centric prompts makes the findings immediately relevant to drivers: it examines how chatbots translate technical issues into layman guidance, estimate costs, flag safety risks, and recommend next steps. That design both reflects the real‑world use case and introduces a natural limitation: the chatbots were not performing hands‑on diagnostics or reading vehicle telematics; they were interpreting descriptions and providing advice accordingly, which is a critical distinction for readers to understand.
Key findings — what each chatbot did well (and where they differed)
The testing team reported a consistent pattern across platforms, with some notable variations:- ChatGPT produced the most thorough, context‑rich answers, providing layered reasoning that helped an owner weigh repair costs, potential safety implications, and whether to seek a professional inspection. This output style earned praise for helping owners make more nuanced decisions.
- Google AI Overview delivered short, factual answers: concise, direct, and pragmatic. Its replies tended to be less expansive but more to the point — useful when a quick, factual readout is what the user needs.
- Microsoft Copilot produced responses that were described as more conversational and personable, but sometimes less precise in technical specificity compared with ChatGPT. That tone may help engagement but can risk glossing over edge‑case safety caveats unless explicitly prompted.
Strengths identified: why AI can help motorists
AI assistants demonstrated several concrete advantages for drivers:- Rapid triage and prioritization — Chatbots can quickly separate safety‑critical faults (e.g., compromised braking or steering) from lower‑priority comfort issues (e.g., malfunctioning air‑conditioning), helping owners decide if immediate towing or driving is unsafe.
- Context and cost framing — Good responses offered contextual factors (vehicle age, mileage, cost of common parts, labor considerations) that help owners decide between repair, delayed maintenance, or scrapping. This framing can reduce the time owners spend searching multiple pages for ballpark figures.
- Encouraging safe behaviour — The chatbots tended to default toward recommending professional inspection when safety‑critical systems were described, which aligns with a cautious, harm‑minimising posture that benefits non‑expert users.
- Accessibility and convenience — For owners without immediate access to a trusted workshop or who need to triage weekend issues, conversational AI provides an always‑on, non‑judgmental first pass that can accelerate the decision to call roadside assistance or a mechanic.
Risks and critical caveats — where chatbots fall short
The SCC testing and mechanic commentary identified a set of important risks that should temper enthusiasm:- False confidence from DIY encouragement. Some chatbot outputs suggested do‑it‑yourself approaches for problems that require tools, skills, or diagnoses only possible with specialist equipment. This can give owners a false sense of competence and lead to unsafe, incomplete or expensive follow‑on damage. The reviewer specifically warned against users taking on jobs beyond their abilities after reading AI guidance.
- No hands, no sensors, no guarantees. Chatbots make assessments from text descriptions; they cannot inspect brake pad thickness, sense clutch slippage by feel, or read live OBD‑II codes. Where a physical diagnostic is required, AI must defer to a mechanic. Treat any textual diagnosis as provisional.
- Liability and legal ambiguity. Advice that omits regulatory or local legal nuances (for example, roadworthiness standards by jurisdiction) could mislead owners. The study’s scoring included “legality” precisely because legal compliance matters with inspections and roadworthy certificates. AI may not always surface jurisdictional requirements unless prompted.
- Variation across models. The difference between a long, context‑heavy ChatGPT answer and a shorter Google reply or conversational Copilot reply matters: users can draw different inferences depending on the tool’s style, leading to inconsistent decisions. This inconsistency is a practical risk when people use multiple assistants and presume equivalence.
- Data freshness and hallucinations. Chatbots’ knowledge cutoffs and reliance on internet sources create two failure modes: outdated cost or procedural data, and hallucinated specifics (invented part numbers or impossible repair flows). That risk grows when users present edge cases or ask for step‑by‑step instructions for complex mechanical jobs. Exercise caution: when an assistant offers unusual specifics, cross‑check with a trusted mechanic or manufacturer guidance.
Practical guidance — how drivers should use chatbots for car problems
To get the benefit of AI while avoiding the pitfalls, the following practical workflow balances convenience with safety:- Describe the symptoms precisely. Note exact warning lights, unusual noises (tone, when they occur), recent repairs, and environmental context (wet, hot, after towing). The more precise the input, the better the chatbot’s conditional advice.
- Ask for safety triage first. Prompt the assistant to rate the urgency (safe to drive, needs inspection today, immediate tow). If the response is ambiguous, default to “seek professional help.”
- Request a non‑technical summary for decision‑making. Ask the assistant to list likely causes, estimated cost ranges, and whether a diagnostic tool (OBD‑II scan, pressure test) is needed.
- Use AI to prepare for the garage visit. Have the chatbot draft questions to ask the mechanic, list likely parts to check, and summarize what to expect in labor time and cost.
- Avoid step‑by‑step mechanical instructions for complex tasks. For anything involving structural safety systems (brakes, steering, airbags, structural bodywork), insist on professional repair; use AI only to understand scope and consequences.
- Cross‑check surprising specifics. If an assistant lists a part number, torque value, or unusual diagnostic step, verify with a parts supplier, official service manual, or a qualified mechanic before acting.
For garages and aftermarket businesses: operational and business opportunities
Mechanics, garages, and parts retailers can turn this emerging behaviour into advantage rather than threat:- Pre‑appointment intelligence. Encourage customers to bring AI summaries of symptoms and questions; this can speed up triage and billing by giving the technician a clearer starting point.
- Offer verified AI prompts. Shops can publish template prompts that customers should use (what info to include, what photos to attach) which improves the quality of owner‑provided data.
- Virtual pre‑checks. Use AI assistants internally to draft likely diagnostic steps for junior techs — but pair with governance to avoid over‑automation.
- Monetize verification. Providing a short ‘AI‑verified’ remote triage service or paid tele‑diagnostic call can be a new revenue line, especially for rural customers.
Legal, safety and consumer‑protection implications
The study’s inclusion of a legality score underlines an uncomfortable truth: advice that touches on inspections, roadworthiness, and safety obligations can have legal ramifications. Regulators and consumer groups will look closely at where AI advice intersects with statutory duties — for example, required annual inspections, emissions rules, or local rules about driving with a known defect.Manufacturers and platforms may also face pressure to label advice with explicit disclaimers and push users toward jurisdiction‑specific guidance. For drivers, the practical upshot is clear: treat chatbot outputs as informational, not authoritative, and follow statutory inspection routes and certified repair channels for anything that affects legal roadworthiness.
What the mechanic reviewers said — notable quotes and interpretation
Tim Singer, the workshop manager who graded the responses, repeatedly emphasised surprise at the quality and safety posture of the chatbots, stating there was “nothing in the answers that would put a motorist in danger” and that the outputs could change how motorists seek help. He also warned of a “false sense of confidence” if non‑mechanics attempt complex repairs after reading AI guidance — a balanced take that celebrates accessible guidance while recognising limits of non‑hands‑on diagnostics.Those two points — capability and limitation — form the story’s most important practical takeaway: AI can do useful, contextually rich triage, but it cannot yet replace the sensor‑based work of a garage or the professional judgement that comes from touch and test.
Broader implications: will AI change how people interact with vehicle maintenance?
The SCC study represents a microcosm of a larger trend: conversational AI is maturing into a first‑contact diagnostic tool across domains. For vehicles, that means:- Fewer needless trips. Better triage could reduce unnecessary visits for minor, non‑urgent issues.
- Better preparation for repairs. Owners may arrive at a shop better informed, improving transparency and reducing friction.
- Increased DIY risk. Conversely, if AI underestimates tool‑needs or over‑simplifies repair complexity, owners may attempt inappropriate DIYs, increasing safety and warranty risks.
- Market segmentation. Distinct user cohorts will adopt AI differently: cautious owners will use it only as a prompt aggregator, while hands‑on hobbyists might push chatbots further into stepwise instructions — raising safety governance questions.
Recommendations: policy, platforms and manufacturers
To capture benefits while reducing harm, stakeholders should consider the following actions:- Platforms should add clear, context‑sensitive disclaimers and ask follow‑up diagnostic questions before offering repair steps.
- Automotive manufacturers and parts suppliers should publish easily accessible, machine‑readable service bulletins and verified diagnostic flows to reduce hallucination risk.
- Trade bodies and regulators should define minimal expectations for when AI advice must refer users to certified inspection or towing.
- Garages should develop customer intake protocols that accept and validate AI‑generated symptom summaries, turning the technology into a workflow booster rather than a competitor.
Final analysis — measured enthusiasm, tempered by risk
The SCC comparison reveals a practical truth about modern conversational AI: in controlled, owner‑facing scenarios, current chatbots are capable and cautious enough to provide useful, safety‑aware first‑line advice — with ChatGPT noted for contextual depth, Google AI Overview for brevity and factual focus, and Microsoft Copilot for a conversational tone that can aid comprehension. Yet, the key constraint remains unchanged: no text‑only assistant can substitute for hands‑on diagnostics, physical measurements, or legally required inspections.For motorists, the pragmatic approach is to use AI as an intelligent aide for triage and preparation, not as a DIY manual for safety‑critical fixes. For garages and industry, the arrival of competent conversational AI presents an operational opportunity to streamline intake, educate customers, and reframe the customer journey — but only if it is integrated with robust verification, clear liability rules, and consumer protections designed to prevent unsafe DIY behaviour.
In short: AI can speed up the conversation around vehicle care — but the workshop, with its tools, sensors and certified technicians, still runs the repair shop.
Source: WhichCar ChatGPT ‘surprisingly accurate’ at giving car repair advice, study finds
