Last month I wrote about my decision to use an AI diary as part of assessment for a module I teach on the journalism degrees at Birmingham City University. The results are in — and they are revealing.

What if we just asked students to keep a record of all their interactions with AI? That was the thinking behind the AI diary, a form of assessment that I introduced this year for two key reasons: to increase transparency about the use of AI, and to increase critical thinking.

The diary was a replacement for the more formal ‘critical evaluation’ that students typically completed alongside their journalism and, in a nutshell, it worked. Students were more transparent about the use of AI, and showed more critical thinking in their submissions.

But there was more:

• Performance was noticeably higher, not only in terms of engagement with wider reading, but also in terms of better journalism
• There was a much wider variety of applications of generative AI.
• Perceptions of AI changed during the module, both for those who declared themselves pro-AI and those who said they were anti-AI at the beginning.
• And students developed new cross-industry skills in prompt design.

It’s not just that marks were higher — but why

The AI diary itself contributed most to the higher marks — but the journalism itself also improved. Why?

Part of the reason was that inserting AI into the production process, and having to record and annotate that in a diary, provided a space for students to reflect on that process.

This was most visible in pre-production stages such as idea generation and development, sourcing and planning. What might otherwise take place entirely internally or informally was externalised and formalised in the form of genAI prompts.

This was a revelation: the very act of prompting — regardless of the response — encouraged reflection.

In the terms of Nobel prize-winning psychologist Daniel Kahneman, what appeared to be happening was a switch from System 1 thinking (“fast, automatic, and intuitive”) to System 2 thinking (“slow, deliberate, and conscious, requiring intentional effort”).

For example, instead of pursuing their first idea for a story, students devoted more thought to the idea development process. The result was the development of (and opportunity to choose) much stronger story ideas as a result.

Similarly, more and better sources were identified for interview, and the planning of interview approaches and questions became more strategic and professional.

These were all principles that had been taught multiple times across the course as a whole — but the discipline to stop and think, reflect and plan, outside of workshop activities was enforced by the systematic use of AI.

Applying the literature, not just quoting it

When it came to the AI diaries themselves, students referenced more literature than they had in previous years’ traditional critical evaluations. The diaries made more connections to that literature, and showed a deeper understanding of and engagement with it.

In other words, students put their reading into practice more often throughout the process, instead of merely quoting it at the end.

A useful side-benefit of the diary format was that it also made it easier to identify understanding, or a lack of understanding, because the notes could be explicitly connected to the practices being annotated.

It is possible that the AI diary format made it clearer what the purpose of reading is on a journalism degree — not to merely pass an assignment, but to be a better journalist.

The obvious employability benefits of developing prompt design skills may have also motivated more independent reading — there was certainly more focus on this area than any other aspect of journalism practice, while the least-explored areas of literature tended to be less practical considerations such as ethics.

Students’ opinions on AI were very mixed — and converged

Surveys taken at the start and end of the module found that students’ feelings about AI became more sophisticated: those with anti- or pro-genAI positions at the start expressed a more nuanced understanding at the end. Crucially, there was a reduction in trust in AI, which has been found to be important for critical thinking.

An AI diary allows you to see how people really use technology

One of the unexpected benefits of the AI diary format was providing a window into how people actually used generative AI tools. By getting students to complete diary-based activities in classes, and reviewing the diaries throughout the module (both inside and outside class), it was possible to identify and address themes early on, both individually and as a group. These included:

• Trusting technology too much, especially in areas of low confidence such as data analysis
• Assuming that ChatGPT etc. understood a concept or framework without it being explained
• Assuming that ChatGPT etc. was able to understand by providing a link instead of a summary
• A need to make the implicit (e.g. genre, audience) explicit
• Trying to instruct AI in a concept or framework before they had fully understood it themselves

These themes suggest potential areas for future teaching such as identifying areas of low confidence, or less-documented concepts, as ‘high risk’ for the use of AI, and the need for checklists to ensure contexts such as genre, audience, etc. are embedded into prompt design.

There were also some novel experiments which suggested new ways to test generative AI, such as the student who invented a footballer to check ChatGPT’s lack of criticality (it failed to challenge the misinformation).

Barriers to transparency still remain

Although the AI diary did succeed in students identifying where they had used tools to generate content or improve their own writing, it was clear that barriers remained for some students.

I have a feeling that part of the barrier lies in the challenge genAI presents to our sense of creativity. This is an internal barrier as much as an external one: in pedagogical terms, we might be looking at a challenge for transformative learning — specifically a “disorienting dilemma”, where assumptions are questioned and beliefs are changed.

It is not just in the AI sphere where seeking or obtaining help is often accompanied by a sense of shame: we want to be able to say “I made that”, even when we only part-authored something (and there are plenty of examples of journalists wishing to take sole credit for stories that others initiated, researched, or edited).

Giving permission will not be enough on its own in these situations.

So it may be that we need to engage more directly in these debates, and present students with disorienting dilemmas, to help students arrive at a place where they feel comfortable admitting just how much AI may have contributed to their creative output. Part of this lies in acknowledging the creativity involved in effective prompts, ‘stewardship‘, and response editing.

Another option would be to require particular activities to be completed: for example, a requirement that work is reviewed by AI and there be some reflection on that (and a decision about which recommendations to follow).

Reducing barriers to declaration could also be achieved by reducing the effort required, by providing an explicit, structured ‘checklist’ of how AI was used in each story, rather than relying solely on the AI diary to do this.

Each story might be accompanied by a table, for example, where the student declares ticks a series of boxes indicating where AI was used, from generating the idea itself, to background research, identifying sources, planning, generating content, and editing. Literature on how news organisations approach transparency in the use of AI should be incorporated into teaching.

AI generation raises new challenges around editing and transparency

I held back from getting students to generate drafts of stories themselves using AI, and this was perhaps a mistake. Those who did experiment with this application of genAI generally did so badly because they were ill-equipped to recognise the flaws in AI-generated material, or to edit effectively. And they failed to engage with debates around transparency.

Those skills are going to be increasingly important in AI-augmented roles, so the next challenge is how (and if) to build those.

The obvious problem? Those skills also make it easier for any AI plagiarism to go undetected.

There are two obvious strategies to adopt here: the first is to require stories to be based on an initial AI-generated draft (so there is no doubt about authorship); the second is to create controlled conditions (i.e. exams) for any writing assessment where you want to assess the person’s own writing skills rather than their editing skills.

Either way, any introduction of these skills needs to be considered beyond the individual module, as students may also apply these skills in other modules.

A module is not enough

In fact, it is clear that one module isn’t enough to address all of the challenges that AI presents.

At the most basic level, a critical understanding of how generative AI works (it’s not a search engine!), where it is most useful (not for text generation!), and what professional use looks like (e.g. risk assessment) should be foundational knowledge on any journalism degree. Not teaching it from day one would be like having students starting a course without knowing how to use a computer.

Designing prompts — specifically role prompting — provides a great method for encouraging students to explore and articulate qualities and practices of professionalism. Take this example:

"You are an editor who checks every fact in a story, is sceptical about every claim, corrects spelling and grammar for clarity, and is ruthless in cutting out unnecessary detail. In addition to all the above, you check that the structure of the story follows newswriting conventions, and that the angle of the story is relevant to the target audience of people working in the health sector. Part of your job involves applying guidelines on best practice in reporting particular subjects (such as disability, mental health, ethnicity, etc). Provide feedback on this story draft..."

Here the process of prompt design doubles as a research task, with a practical application, and results that the student can compare and review.

In the rapidly evolving landscape of technology, artificial intelligence (AI) is playing a pivotal role in shaping global dynamics. Traditionally, the United States has been at the forefront of AI innovation and deployment. However, recent developments indicate a significant shift in this landscape. China is making remarkable strides in AI, challenging American dominance and establishing itself as a formidable competitor in the global AI race. This article explores the factors contributing to China’s rise in AI, the implications for the United States, and the broader global impact of this technological competition.

China’s Strategic Investments in AI

China’s government has recognized the immense potential of AI and has made it a national priority. The country has invested billions of dollars in AI research and development, with the aim of becoming the world leader in AI by 2030. Chinese tech giants like Alibaba, Tencent, and Baidu are at the forefront of this push, developing cutting-edge AI technologies and implementing them across various sectors.

Moreover, China’s AI strategy is supported by state-backed initiatives and favorable policies that encourage innovation and deployment. These include subsidies, tax incentives, and the establishment of AI research centers. The government’s support is complemented by a vast pool of data generated by its large population, which serves as a crucial asset for training AI models. This combination of investment, policy support, and data availability positions China as a formidable player in the AI arena.

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The Erosion of America’s AI Lead

The United States has long been the leader in AI, driven by its robust ecosystem of universities, tech companies, and research institutions. However, several factors are contributing to the erosion of America’s lead in AI. One significant factor is the brain drain of AI talent. Many skilled researchers and engineers are being lured to China by attractive compensation packages and the opportunity to work on groundbreaking projects.

Additionally, US regulations concerning data privacy and export controls are perceived as restrictive, limiting the ability of American companies to compete globally. In contrast, China’s regulatory environment is more favorable to rapid AI development. These challenges, coupled with China’s aggressive investments, are creating a scenario where the US is facing increasing competition from China in the AI sector.

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Global Implications of the AI Race

The intensifying AI race between the United States and China has significant global implications. As China advances in AI, it becomes a key player in shaping international standards and practices. Chinese AI solutions are gaining traction not only domestically but also in regions like Europe, the Middle East, and Africa. This widespread adoption of Chinese AI technology signals a shift in the balance of technological influence.

This development also raises concerns about the geopolitical implications of AI leadership. As AI becomes integral to national security and economic growth, countries are increasingly viewing technological leadership as a matter of strategic importance. Consequently, the AI race is transforming into a new form of global competition, akin to an arms race, where technological prowess is pivotal to national power.

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The Role of Innovation and Collaboration

Despite the competition, there is an opportunity for collaboration and innovation between the United States and China. Joint research initiatives and partnerships between companies from both countries can drive forward the development of AI technologies. Collaborative efforts can help address global challenges such as climate change, healthcare, and cybersecurity, where AI can play a transformative role.

However, for collaboration to be effective, there must be mutual respect for intellectual property rights and a commitment to ethical AI practices. Finding common ground in these areas could pave the way for a more cooperative and less adversarial relationship in the AI domain. Such collaboration could ultimately benefit not only the US and China but the global community as a whole.

The rapid advancements in AI technology are reshaping the global landscape, with China emerging as a formidable competitor to the United States. As the AI race intensifies, the implications for global power dynamics, innovation, and collaboration are profound. With both countries striving for technological supremacy, how will this competition shape the future of AI, and what will be the long-term impact on international relations and global technological standards?

Our author used artificial intelligence to enhance this article.

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Meta CEO Mark Zuckerberg is on a hiring spree. Recently, the Meta CEO hired some top engineers from OpenAI and also recruited Daniel Frost, former CEO and co-founder of Safe Superintelligence at his newly formed Meta Superintelligence Labs. Now, Zuckerberg’s aggressive hiring plan has reportedly given a blow to Apple. According to a report by Bloomberg, Meta has hired Ruoming Pang a noted engineer and manager who led the artificial intelligence team at Apple. Pang is reportedly joining Meta Platforms, marking another high-profile acquisition in Meta’s aggressive AI talent recruitment drive.

Meta is reportedly hiring Apple’s head of foundation models, Ruoming Pang

As reported by Bloomberg, Meta is hiring Pang for its AI expansion program. Meta’s pursuit of Pang was reportedly intense, with the social media giant offering a compensation package valued at “tens of millions of dollars per year.” This substantial offer underscores the fierce competition for top AI talent in the industry, with Meta willing to pay significantly more than Apple for similar expertise.The report also adds that Meta CEO Mark Zuckerberg has been personally involved in this hiring spree. Zuckerberg has been hosting potential recruits and is also actively reaching out to secure major AI roles. Pang joined Apple in 2021 and held the responsibility of managing a team of 100 people. He worked on developing large language model which power Apple Intelligence and other AI features across Apple devices. These models took care of functionalities such as email and web article summaries, Genmoji, and Priority Notifications, and were recently opened up to third-party developers for the first time.As per report, it is said that Mark Zuckerberg offered a compensation package worth tens of millions of dollars annually to secure Pang. Pang joins a growing list of elite hires at Meta, including Alexandr Wang, Daniel Gross, Nat Friedman, Yuanzhi Li (OpenAI), and Anton Bakhtin (Anthropic).

Effect of Ruoming Pang exit on Apple’s AI division

Pang’s exit from Apple comes with a crucial time. Internally, Pang’s foundation models team, also known as AFM, has faced scrutiny from new leadership exploring the integration of third-party models, potentially from OpenAI or Anthropic, to power a new version of Siri. These internal discussions have reportedly impacted morale within the AFM group, with several engineers indicating plans to leave. Tom Gunter, a key deputy to Pang, also departed Apple last month.