AI Confidential with Hannah Fry - hatchet job that lacks balance

AI Confidential with Hannah Fry is suspect. None of the three episodes makes any attempt at comparing upsides and downsides. Almost all of the time is spent, often laboriously, on the downsides, so they come across as hatchet jobs. For a documentary series on AI, there is a serious lack of data, studies and balance. That’s a shame.

Episode 1  AI girlfriends

First episode, on Jaswant, who broke into Windsor Castle (and was caught) was just weird. He had visited Amritsar, site of a massacre of Indians in 2018, became obsessed about colonial injustice, failed his exams, lonely during COVID, depressed, thought he was a Sith Lord and was seriously psychotic, so mentally ill that he was diagnosed as being in full blown psychosis when he climbed the wall and was eventually not charged with the crime but locked up in Broadmoor. 

Yet his Replica girlfriend bot is presented as the cause, a primitive pre-LLM ELIZA. The problem is a complete lack of balance and consideration of multiple causes. The police thought he would have done this anyway without the bot. They find this obsessive behaviour in many young terrorists. The mistake, common when fingering AI, is to see such things in terms of a single cause, when it is clearly muti-variant. And when things are multivariant, the new kid on the block, in this case AI, gets the blame.

I’d recommend you read ‘Love Machines’ by researcher James Muldoon, who takes a wider look at how bots are used by millions as companions, friends, girlfriends, lovers, mentors, therapists, advisors, coaches, and deathbots. There’s good evidence to show that far from manufacturing assassins, the technology helps with loneliness, avoiding embarrassment, as it is non-judgemental and affirmative. People feel heard, understood and supported, as the bots are calm and anonymous. They can break the silence and stave off the isolation. It is balanced in that it compares the upsides and downsides. Fry’s three programmes are all lopsided.

Episode 2 Self-driving cars

Rafaela Vasquez, the safety driver in Uber's self-driving Volvo, was distracted and looking down at her personal cell phone, even before she was on the road, streaming an episode of ‘The Voice’ on Hulu. In the moments leading up to the crash, that killed pedestrian Elaine Herzberg,  Dashcam footage showed her looking down for about 5.3 seconds immediately before the impact. and the National Transportation Safety Board determined she spent roughly 34% of the trip looking at her phone rather than monitoring the road. The police deemed the crash ‘entirely avoidable’ if she had been attentive. Vasquez was charged with negligent homicide in 2020 and pleaded guilty in 2023. She pleaded guilty and was sentenced to three years of supervised probation. Fry at this point blamed the car, when this was clearly a human error. You’d think she was innocent when listening to Fry. She may be a mathematician but she’s no journalist. 

Enter George McGee was found culpable in the civil lawsuit stemming from the April 2019 fatal crash in Key Largo, Florida. He admitted to police that he dropped his phone, while driving on ‘cruise’, and looked down to retrieve it while driving his Tesla on Autopilot. Note he was using Tesla's Autopilot at the time of the 2019 crash, not Full Self-Driving, which was not available until 2020.

This caused the vehicle to run a stop sign at around 62 mph and strike a parked SUV. This killed Naibel Leon and injured her boyfriend, Dillon Angulo. A jury in 2025 assigned him the majority of the blame 67%, with Tesla at 33%, and he settled privately with the plaintiffs prior to the trial against Tesla. It is true that Tesla paid the dead woman’s family a massive amount of money but this is a complicated case of a largely culpable driver and some AI.

In a California incident, they did not mention that the NTSB investigation revealed that Autopilot was engaged for nearly 19 minutes prior to the crash, and Huang's hands were not detected on the steering wheel for the six seconds before impact; data also showed he was playing a video game on his phone during the drive. 

In the Utah incident, Heather Lommatzsch's Tesla Model S, with Autopilot engaged, rear-ended a stationary fire truck while she was looking at her phone. She sustained a broken foot and the fire truck driver reported minor whiplash; no fatalities. Police determined she was culpable for distraction and over-reliance on Autopilot, issuing her a misdemeanour traffic citation for failure to keep a proper lookout. 

Overall, this was a hatchet job. No overall safety stats, which are positive but happy to feature footage of nutjob luddites in balaclavas. 

Episode 3 Healthcare

This was the strangest of all. As it was not AI in the dock but a US medical insurance company who were gouging customers. It’s a business model, not AI to blame. Sure they were denying people proper care but this is a feature of the American system, that maximises profit not care.

It was a bait and switch story, where the murder of the CEO by Luigi Amlioni, was warped into AI being the villain. Al was certainly being used but if this were spreadsheet with a formula, even humans making these decisons, the bottom line is greed not maths. Again, no balance.

I did a lot of research on AI in healthcare for my book AI and Productivity and have an entore chapter on the subject. I feel that Fry has focused too much on one case, in one business, in one country, doing one task, when there is evidence that AI is being beneficial across many areas in healthcare.

Conclusion

I’ve given up on the BBC Radio 4 stuff on AI, as it is truly awful, but expected more from Fry, as she’s a good presenter, smart and has the background to understand the technology. What she doesn’t have is the journalistic training and experience to see the big picture, so this series descends into rather long-winded hatchet jobs.

AI fits like a glove in EFL and second language teaching

“AI is your friend, not your enemy” was the title to my Keynote. That statement can sound provocative in a hall full of language teachers. We have all seen the headlines warning that translation tools are destroying motivation, that chatbots are replacing writing, that students are outsourcing their thinking. It is understandable to feel cautious. Yet when we look at the evidence and, more importantly, at what is actually happening in classrooms and with actual teachers and language learners, a more balanced and optimistic picture emerges.

There is a persistent fear that instant translation will remove the need to learn another language. If a phone can translate, why struggle with grammar, vocabulary and pronunciation? And yes, some learners report that translation tools reduce their motivation, but most do not. The majority remain motivated to learn languages even while using AI tools. Large international surveys of teachers show that human-led language learning remains irreplaceable, and scepticism that AI will replace teachers any time soon. Motivation has not collapsed. It has shifted. The environment has changed, and our role as teachers is not to resist that environment but to shape how students learn within it.

The reality is that students are already using AI, and many are using it daily. They practise speaking and receive feedback, summarise texts, brainstorm ideas, generate content, build flashcards, simulate exams and converse with chatbots. For many learners, AI has quietly become a personal assistant. Ignoring this will not make it disappear. Harnessing it, however, can transform our teaching.

One of AI’s most powerful contributions is psychological. Language learning is emotionally demanding. Students fear embarrassment. They worry about making mistakes. They hesitate to speak. AI provides a non-judgemental partner that is infinitely patient. It offers immediate help, avoids public correction, reduces anxiety and gives affirmative feedback. Learners can rehearse privately before speaking in class. For shy or anxious students, this is not a minor advantage; it can be transformative. Increased confidence often leads to increased engagement and engagement is the engine of progress.

The research evidence supports this. I showed the two major teta-studies examining AI tools in English language learning that report significant improvements in achievement. Reviews of AI-powered chatbots for speaking practice describe strong effects on oral proficiency, interaction and motivation. We are not discussing novelty tools; we are seeing measurable impact. When AI enhances interaction, it enhances language learning, because language learning is interaction.

Voice-based AI is particularly powerful for EFL. A learner can say, “Be my Spanish tutor at B1. Speak slowly, use everyday topics and stop often with questions,” or “I’m A2 French. Keep sentences short and correct me after I finish.” They can switch between languages, practise hotel check-in roleplays, request dictation, drill minimal pairs such as “ship” and “sheep,” practise connected speech like “Whaddaya wanna do?” or complete article drills choosing between “a,” “an,” “the” and zero article. The feedback is immediate and personalised. Each learner can operate at their own level, repeat as often as needed and progress at their own pace. This is differentiation without leaving anyone behind.

AI fits beautifully with Papert’s principle of high ceiling, low floor and wide walls interface. A beginner can have simple, structured conversations – low floor An advanced learner can debate ideas, analyse arguments or rehearse professional interviews – high ceiling The same tool accommodates a wide range of ability levels and creative directions – wide walls.

There is also a deeper pedagogical reason why AI works so naturally in language teaching. From Socrates onward, learning has been understood as dialogue. Socratic questioning draws out thinking. Bakhtin emphasised that meaning emerges through multiple voices in interaction. Vygotsky described the “knowledgeable other” who mediates learning within the learner’s Zone of Proximal Development. AI does not replace the teacher, but it can function as an additional knowledgeable other, available whenever the learner needs it. The teacher remains the orchestrator of learning.

Many teachers are already experimenting. They use AI to create lesson plans, generate materials, personalise exercises, design assessments and increase student engagement. At the same time, many feel underprepared. This is not a reason to retreat; it is a reason to invest in professional development. Teachers need permission to prompt, and students need permission to prompt wisely.

Assessment is another area where AI fits naturally. It can analyse spoken presentations, generate targeted feedback, turn transcripts into personalised error-based flashcards, create grammar drills based on actual student mistakes and help design video-based assessments. What once required hours of marking can now become rapid, formative feedback. Used thoughtfully, AI strengthens rather than weakens assessment for learning.

Perhaps most inspiring is the global dimension. AI systems now support hundreds of languages, including many that have historically lacked digital presence. Initiatives focused on low-resource languages aim to reduce language inequality in technology. Oral traditions can be digitised through mobile devices. Minority languages can gain visibility and vitality online. AI is not simply serving English; it is expanding linguistic possibility.

So where does that leave us as teachers? Precisely where we have always been: at the centre. AI cannot build human relationships. It cannot replace cultural nuance, empathy, humour or inspiration. It can amplify what we do. It gives learners rehearsal space, lowers anxiety, personalises practice, provides role-plays,  and multiplies exposure to language. It gives teachers creative leverage and new forms of feedback. It extends dialogue beyond classroom walls.

AI does not diminish language teaching. Used well, it strengthens it. It fits like a glove. The glove does not replace the hand. It enhances what the hand can do.