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Deciphering authenticity in the age of AI: how AI‑generated

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Deciphering authenticity in the age of AI: how AI‑generated

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Key Point: Generative Artificial Intelligence, Authenticity, Heuristic-systematic model, Disinformation

Personal abstract: This study focuses on how people judge the authenticity of AI-generated images and the impact of AI detection tools on such judgments. Its aim is to provide references for the development of disinformation detection tools and the governance of the spread of deceptive visual content.

The study involved 292 participants based in the UK and presented with different types of AI-generated and non-AI-generated disinformation images, asked to judge the authenticity of the images and assess their confidence in these judgments. Subsequently, the correct verdict from an AI detection tool (Hive Moderation) was provided to observe whether participants would adjust their judgments.

The results indicated that aesthetic realism had a significant impact on authenticity judgments: the more realistic-looking an AI-generated image was, the more likely it was to be judged as authentic, yet participants had lower confidence when making such judgments. In contrast, emotional salience had no significant effect on authenticity judgments. Regarding judgment adjustments, the aesthetic realism of the images and participants' own confidence levels were key factors—higher aesthetic realism made participants more likely to adjust their judgments based on the AI detection tool's results, and lower confidence also led participants to be more inclined to make adjustments. Additionally, younger participants and those with more positive attitudes toward AI were more likely to refer to the verdicts of AI detection tools; participants with extreme attitudes toward AI (either highly positive or highly negative) generally had higher confidence when judging the authenticity of AI-generated images.

Personal Analysis: I think this study explores the key factors influencing people's judgments on the authenticity of such images and the role of AI detection tools in shaping human judgments, in response to the proliferation of AI-generated false images (especially those used in the spread of political disinformation). It provides a basis for disinformation governance and the optimization of detection tools.

Source: https://link.springer.com/article/10.1007/s00146-025-02416-5

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Deciphering authenticity in the age of AI: how AI-generated disinformation images and AI detection tools influence judgements of authenticity - AI & SOCIETY

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An ongoing surge of Artificial Intelligence (AI)-enabled false content has been spreading its way through the information ecosystem, including AI-generated images, which have been used as part of political disinformation campaigns. Thus, there remains a pressing need to understand which factors individuals rely upon when determining whether images are AI-generated, particularly when they can be used to spread disinformation. AI-generated images have been characterised by their aesthetic realism, which can be leveraged to deceive users, and those who use generative AI to create deceptive content also tend to exploit its ability to convey and elicit emotion. This experimental study explored how aesthetic realism and emotional salience, as key features of both AI-generated content and disinformation, may influence authenticity judgements of AI-generated disinformation images. In this study, 292 UK-based participants were presented with both AI-generated and non-AI-generated disinformation images which varied in aesthetic realism and emotional salience. Results showed that participants were more likely to judge realistic-looking AI-generated images as being authentic compared with less realistic-looking AI-generated images, but did so with less confidence in their decision. Emotional salience was not a significant predictor of judgements. When participants were presented with the correct verdict of an AI detection tool, their reliance on the tool to update their own judgements was predicted by the aesthetic realism of the image and their confidence levels. These findings may assist with the development of disinformation detection tools, as well as strategies that mitigate the spread of deceptive, synthesised visual content in the digital age.

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Digital learning in the 21st century: trends, challenges, and innovations in technology integration

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Digital learning in the 21st century: trends, challenges, and innovations in technology integration

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Key Points: Digital Learning, Education, Digital Technology

Personal Abstract:

The article highlighted how the convergence of technologies such as artificial intelligence, virtual reality, and online platforms has significantly enhanced learning accessibility, engagement, and personalization. It substantiated their positive impact on student outcomes through cutting-edge case studies, including adaptive learning systems and the Open University in the UK. Simultaneously, the article delved into critical challenges facing digital learning, including uneven infrastructure, insufficient teacher digital literacy, data privacy concerns, and the digital divide. Finally, the article offered strategic recommendations for educators and policymakers, including strengthening infrastructure investment, prioritizing teacher professional development, and promoting inclusive content design. These measures aim to build an inclusive and effective future learning environment.

Personal Analysis/Application:

The strategic recommendations proposed in this article are pragmatic and instructive, emphasizing the core principles of “equity” and “teacher development” rather than blindly championing technology. A slight shortcoming is that the article's perspectives largely synthesize existing research, with relatively limited original analysis, and its exploration of certain future directions remains somewhat sketchy. Overall, it serves as a valuable reference for understanding the current landscape of digital education development.

Reference:

Zou, Y., Kuek, F., Feng, W., & Cheng, X. (2025). Digital learning in the 21st century: trends, challenges, and innovations in technology integration. Frontiers in Education, 10, 1562391. https://doi.org/10.3389/feduc.2025.1562391

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Yumei Zou, Florence Kuek, Wenqin Feng, Xiaoli Cheng

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Frontiers | Digital learning in the 21st century: trends, challenges, and innovations in technology integration

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The integration of digital technologies into education represents a significant evolution in the pedagogical landscape, with the potential to enhance accessi…

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How digital innovation will impact the future of mobility

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How digital innovation will impact the future of mobility

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Key points: Digital Technology, Innovation, Molibility

Personal Abstract:

As trends like global aging intensify, people's lifestyles are undergoing significant changes. According to the United Nations' Global Mobility Report, annual passenger traffic is projected to triple by 2050. Digital innovation is crucial to achieving this goal. It transforms transportation systems from isolated physical modes into integrated, MaaS ecosystems. This enables people to navigate the future world in effortless, convenient, and sustainable ways. However, the article emphasized that realizing this future requires not only technological breakthroughs but also close collaboration between public and private sectors, data sharing, and new regulatory frameworks to address the accompanying challenges.

Personal Analysis:

In my view, the article clearly highlighted that digital innovation in transportation systems manifests through model innovation, though certain aspects appear idealistic. The three major categories of innovation mentioned offer valuable suggestions for future development. User journey boosters, a type of innovation directly targeting end-users, leverage data integration and intelligent algorithms to eliminate uncertainties and complexities in travel. Ticketing solutions eliminate the hassle of queuing for tickets and excessive paper usage, enhancing convenience. Optimization tools enhance the operational efficiency and safety of the entire transportation network while reducing operational costs through predictive monitoring. However, while the article acknowledged that technological innovation requires multi-stakeholder coordination and collaboration, it overlooks risks such as conflicting interests, funding investment, and privacy security, presenting a somewhat idealized perspective.

Reference:

Jain, S., Faraj, H., Chowanetz, M., & Joga, A. (2025, March 28). How digital innovation will impact the future of mobility. KEARNEY. https://www.kearney.com/industry/transportation-travel/advanced-mobility-institute/article/how-digital-innovation-will-impact-the-future-of-mobility

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how digital innovation will impact the future of mobility

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Artificial intelligence is set to revolutionize strategy activities. But as AI adoption spreads, strategists will need proprietary data, creativity, and new skills to develop unique options.

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How AI is transforming strategy development

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How AI is transforming strategy development

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Key Point：

AI, Digital Technology, Strategic Development,

Personal abstract:

This article mainly explores how artificial intelligence fundamentally changes the essence of enterprise strategic planning. Traditionally, strategic development has been a lengthy process that relies on the limited experience and intuition of top managers. However, through AI's rapid analysis of massive internal and external data (such as macroeconomic trends, competitor movements, etc.), it generates more in-depth and objective insights, and even simulates the possible outcomes of different strategic choices. Subsequently, the analysis table in the article clarifies the five key roles that AI plays in the strategic process - Researcher, Interpreter, Thought partner, Simulator, and Communicator. It clearly demonstrates how AI evolves from a tool to a collaborative partner throughout the entire process of strategic design and execution monitoring. This transforms strategic planning from a static and periodic activity to a dynamic and continuously optimized process. Decision-makers also shift from relying on intuition and experience to being adept at using AI insights to pose more precise questions, conduct more rigorous debates, and make more accurate judgments. Ultimately, AI does not replace human judgment but rather guides enterprises toward a new strategic decision-making ecosystem of human-machine collaboration, empowering enterprises to deliver the competitive edge needed to win in the marketplace.

Personal analysis:

By reading this article, we can conclude that in the future, artificial intelligence will completely transform strategic activities. AI can play a significant role in all stages of strategic planning in enterprises. By combining the course outline analysis, the traditional strategic development model has been unable to keep up with the rapidly changing market demands. To build the sustainable competitiveness of enterprises, AI has become an important digital technology for reshaping strategic planning. The article describes that in the strategic planning stage, AI acts as a researcher and simulator, analyzing large amounts of data to generate exclusive strategic insights and simulate different strategic outcomes, so that decision-making is no longer based on intuitive judgment but is based on data deduction. In the mobilize the organization stage, AI assumes the roles of communicator and coordinator, converting strategic intentions into specific measures and transmitting them to different departments, determining priorities and optimizing resource allocation. Finally, in the strategic execution and monitoring stage, AI can help enterprises continuously monitor market feedback and performance, and dynamically adjust strategies to respond to environmental changes.

By combining the description in the course outline of "Digital technologies should drive the transformation of organizations into innovative learning organizations", the article also states that future strategists need to understand the working principles of AI, find the correct methods for applying AI to strategic planning, identify potential risks, even though AI has the most advanced capabilities, it cannot replace human thinking logic and interpretation ability. Mastering digital technologies can enhance our decision-making, but how to integrate it into our long-term development and form something beneficial to our own unique insights still requires us to continuously summarize and reflect in practice.

Source: https://www.mckinsey.com/capabilities/strategy-and-corporate-finance/our-insights/how-ai-is-transforming-strategy-development#

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At its core, strategy entails deriving insights from facts and data, developing real options based on those insights, making hard-to-reverse choices, and executing initiatives that convert those choices into value. Data analytics has assisted in this work for several decades, but never before has technology been able to not only augment and partially automate inputs into strategy but also combine them into complex analyses. In time, it may even recommend viable strategies.

Artificial intelligence and generative AI have the potential to transform how strategists work by strengthening and accelerating activities such as analysis and insight generation while mitigating challenges posed by human biases and the social side of strategy. Building on the recent explosion in data and earlier AI advances that produced dramatic improvements in forecasting accuracy, the latest tools are making deriving insights much easier and cheaper. The impact we are seeing in client organizations and in our own work as strategists leads us to view this moment as a new inflection point in strategy design-potentially on par with the creation of core strategic frameworks in the 1970s and '80s.

While AI won't change the need for leaders to demonstrate strategic courage by committing to big moves, we expect that the technology will, in time, enhance every phase of strategy development, from design through mobilization and execution. Today, the technology delivers the greatest benefits in the design phase, by helping organizations assess their starting point in the context of industry and market dynamics. They can use it to size potential markets, analyze competitor moves, and estimate the value of different strategic initiatives across multiple scenarios. But that's just the beginning: Strategy requires mobilizing the organization, ensuring the right allocation of resources, and monitoring execution. In all these tasks, AI can play a role.

Emerging roles for AI in strategy

Human judgment remains essential to crafting the strategic vision, which combines the organization's ambition with a view of how to realize it. However, AI can accelerate and bring greater rigor to the work of strategy teams. Even in these early days, we see five roles for AI: researcher, interpreter, thought partner, simulator, and communicator. Each of these roles can come into play at various steps across the different phases of strategy development (table):

Researcher. Strategists spend significant time gathering and enriching data from numerous sources. AI's ability to summarize and create meaningful connections across all data sets can significantly enhance these efforts. For example, an AI-powered engine that identifies potential M&A targets can pinpoint under-the-radar assets that fit a company's strategic thesis, enhancing what today is often a serendipitous process relying on executives' and their intermediaries' market knowledge. One such tool can scan public information on more than 40 million companies across various languages and create a short list of relevant targets in minutes. While AI is more thorough and faster than humans, strategists still need to pose the right questions to generate the distinctive insights they seek.

Interpreter. To turn data analytics into useful insights, strategists need to interpret how the findings can advance their goals. For example, a search for growth opportunities often entails looking into adjacencies. Those expansion ideas can come from many places, such as reviews of competitors' moves or a deep understanding of customers' emerging needs. AI tools can facilitate this discovery process by converting data from a disparate set of inputs-such as annual reports, patents, customer reviews, and purchasing data-into "growth scans." These scans summarize the most frequently pursued adjacencies and then interpret and score their fit with the company's strategy. The resulting perspective can help strategists narrow down options, find precedents or benchmarks for actions under consideration, and uncover fresh ideas.

Another area where AI is already acting as an interpreter is trend monitoring. Strategists need to keep tabs on changes in major trends when developing their options and reviewing their assumptions. A gen-AI-powered engine can read massive amounts of information and disaggregate trends into their component patterns and then interpret whether those patterns suggest a trend is accelerating, maturing, or subsiding. For example, an organization seeking to understand the demand for sustainable building materials can monitor interest from architects, patent volumes, and competitors' mentions long before those signals translate into sales volumes.

Thought partner. AI can also serve as a brainstorming partner, speeding up idea generation and countering business leaders' potential biases or blind spots. Gen AI in particular can help strategists avoid common pitfalls by assessing their plans against established frameworks. For example, a team can pressure test a strategy-both before and during its execution-by leveraging gen AI to play a challenger role to highlight potential hidden pitfalls or management blind spots.

Simulator. Before committing to a strategic course, strategists consider the impact of multiple market scenarios based on macroeconomic conditions, potential competitor moves, and stakeholder reactions, among other factors. AI can make this scenario analysis much more rigorous through advanced modeling capabilities and tactical game and simulation applications. This capability can also be valuable during the strategy's execution, with AI monitoring early signals from the market, simulating their impact, and alerting the team when it might be prudent to change course.

Communicator. A clear narrative of the strategic path and objective and their implications for the organization and its stakeholders is essential to mobilizing action. Gen AI's ability to summarize concepts in different formats has been among the technology's most popular applications since ChatGPT was launched. Strategists can use gen AI tools to make their narratives more compelling to different audiences with different levels of expertise (such as regional markets, regulators, or analysts) and in different formats (briefs, talking points, or, most recently, podcasts). AI can also monitor whether external communications are consistent across different channels.

To see how these five applications can work in practice, consider the case of a Southeast Asian regional bank that wanted to expand to a new segment or geography. The strategy team used its AI model to analyze the business context and promising trends in the industry and region. The tool generated interactive reports that allowed the strategists to fine-tune their follow-up research. Based on this work, the strategy team decided to focus on opportunities in the digital financial ecosystem (particularly peer-to-peer payments) and microcredit.

Next, the team asked AI to provide recommendations on the most promising adjacencies for growth investments. Based on an analysis of information from banks around the world, the tool created a graph of close and synergistic business segments. Management prioritized a few for deeper analysis-for example, a cross-border digital offering across the region or the microcredit segment in Vietnam-and built hypotheses on their potential growth trajectories. To learn more about each segment, they asked AI, "Who are my competitors in each market, and what are their value propositions?" Some of the markets were unfamiliar to the leaders, so the strategy team posed questions such as, "We are considering entering the Vietnamese banking market. What are the risks that have emerged in the past? Are there examples of failed attempts (with sources)?"

The team also considered inorganic options such as partnerships and M&A. Based on an AI scan, they short-listed a few small and medium-size businesses with the technology the company needed to support its digital ambition. Gen AI also helped them build initial due diligence profiles to support potential outreach.

Finally, as hypotheses solidified into concrete strategic options, AI helped the strategists simulate the resulting P&L and growth projections. Additionally, the tool utilized internal data, such as management reports on the bank's earlier expansion into another country, to help management understand the strengths and weaknesses of their execution capabilities.

Numerous organizations have started building tools to make such scenarios a reality, with some developing proprietary AI agents to simulate reasoning or perform complex research tasks. However, even those earlier in their AI journey can start exploring some of the roles that AI can play. As technology advances, strategists who build the skills to develop unique applications for AI models will gain a critical insights edge over competitors.

Considerations for strategy leaders deploying AI

While the journey of the Southeast Asian bank is compelling, strategists should be mindful of several challenges in deploying AI. Generative AI presents well-documented risks, ranging from model bias (historical training data can lead AI to overemphasize certain types of customers, for example) to reduced explainability (failure to offer a logical foundation for the analysis) to hallucinations (constructing credible-sounding but false content). The good news is that each of these pitfalls is being addressed. For example, AI can help police itself: A "critic agent" can check the work done by other AI applications and flag when the content might be incorrect or directly instruct a reworking of the task in question.

Beyond these well-understood risks, gen AI presents five additional considerations for strategists. First, it elevates the importance of access to proprietary data. Gen AI is accelerating a long-term trend: the democratization of insights. It has never been easier to leverage off-the-shelf tools to rapidly generate insights that are the building blocks of any strategy. As the adoption of AI models spreads, so do the consequences of relying on commoditized insights. After all, companies that use generic inputs will produce generic outputs, which lead to generic strategies that, almost by definition, lead to generic performance or worse. As a result, the importance of curating proprietary data ecosystems (more on these below) that incorporate quantitative and qualitative inputs will only increase.

Second, the proliferation of data and insights elevates the importance of separating signal from noise. This has long been a challenge, but gen AI compounds it. We believe that as the technology matures, it will be able to effectively pull out the signals that matter, but it is not there yet.

Third, as the ease of insight generation grows, so does the value of executive-level synthesis. Business leaders-particularly those charged with making strategic decisions-cannot operate effectively if they are buried in data, even if that data is nothing but signal. As with gen AI's growing ability to separate signal from noise, the technology is getting better at synthesis, but in the near term, strategy leaders need to own that task.

Fourth, AI reinforces the importance of the processes that organizations follow to develop their strategies. Our research shows that the quality of the process is far more important to strategies' success than the quality of insights. High-quality processes include, but are not limited to, the development and examination of strategic alternatives, properly accounting for uncertainty, pushing to make bold commitments, and, most importantly, taking steps to remove bias from decisions. Fortunately, as gen AI speeds up the development of insights, it leaves more time for strategy teams to hone best-in-class processes.

Finally, to successfully leverage gen AI, the strategy function needs to invest in technology for creating and accessing ecosystems of proprietary data sources. The ecosystem approach removes the need for companies to internally generate or own the full gamut of proprietary data. Instead, they build networks of sources that they can seamlessly tap into using technology. In addition, strategists will need to identify (and often customize) gen AI tools that can effectively serve as researchers, simulators, interpreters, thought partners, and communicators.

Moving forward

So where do you begin? We recommend three near-term steps:

Get smart. The strategist of tomorrow needs to understand how AI works. How does a word prediction engine manipulate complex concepts and information? How are insights generated from the information included in models and prompts? Those who gain this expertise will be able to contribute to creating the tools their work requires, such as running complex simulations on how markets and competitive landscapes will evolve. Individuals with such skills will be highly sought after, making their retention a management priority.

Start building today. AI is here to stay, and finding the right way to apply it to strategy development is essential. Strategy teams should familiarize themselves with the possibilities AI offers, from helping in their research and insight generation to identifying potential risks. Teams that explore how the available tools can assist in these tasks will better understand what other tools they will need to build or invest in to meet their specific needs. From an organizational perspective, leaders need to help strategy teams gain access to expertise in data science, data engineering, and large language models. This can be done by embedding technology experts into strategy teams or by providing strategists access to them through centers of excellence.

Develop your proprietary insights ecosystem. Even with state-of-the-art capabilities, AI models will be limited to interpreting existing data-they cannot generate new signals. For example, AI won't replace the insights from ethnographic research or the direct input from customers. Indeed, such proprietary information will become even more critical to generating unique insights as external data grows more affordable and accessible to all market participants. To gain an edge, strategists will need to expand their exposure to different domains by connecting with innovators and stakeholders within and outside their organizations. Strategists' core focus will increasingly become developing hypotheses, testing and learning from them, and maintaining the AI and data infrastructure that enable the conversion of insights into a competitive advantage.

Artificial intelligence can't-and, we believe, won't-replace human logic and interpretation in a complex domain, such as strategy. However, the technology can provide faster, more objective answers that can significantly augment our decision prowess. Through the various roles AI can already play, from researcher to thought partner to simulator, we are starting to see how these tools may, in time, redefine strategists' roles and help companies make strategic decisions. By making the strategy development process more efficient while allowing the space for creativity and breakthrough ideas that help leaders define the consequent bold moves, AI can deliver the competitive edge needed to beat the market.

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How AI is transforming strategy development

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Artificial intelligence is set to revolutionize strategy activities. But as AI adoption spreads, strategists will need new skills to develop unique options.

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The analysis of strategic management decisions and corporate competitiveness based on artificial intelligence

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The analysis of strategic management decisions and corporate competitiveness based on artificial intelligence

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Key Points: Artificial intelligence, Strategic management decisions, Hybrid optimization model, Model performance evaluation

Personal abstract: To address the issues of traditional corporate strategic decision-making, which relies on managers' experience and struggles to handle complex data and high-frequency market fluctuations, this study proposes a hybrid optimization model, aiming to improve decision accuracy and efficiency as well as enhance corporate competitiveness.

The study first reviews relevant literature to clarify the value of AI in decision support, then constructs the model through steps such as data collection and preprocessing, algorithm selection and modeling, and model training and validation. Finally, use real-world data to evaluate the model's performance in terms of training time, convergence speed, and prediction effectiveness. Results show that the model converges within 150 iterations, achieving prediction accuracies of 92%, 91%, and 89% for market share, profit growth rate, and customer satisfaction respectively. After the model's application, the enterprise's market position, brand influence, and technological innovation capabilities are significantly improved.

The study confirms that the model can enhance decision scientificity and corporate competitiveness, but it still have restrictions. Future research needs to expand the scope of application.

Source: https://www.nature.com/articles/s41598-025-02842-x

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The analysis of strategic management decisions and corporate competitiveness based on artificial intelligence - Scientific Reports

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Scientific Reports - The analysis of strategic management decisions and corporate competitiveness based on artificial intelligence

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Higher education institutions face stiff competition in the race for talent. A strong and connected HR function could give them an edge.

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Transforming HR to better support higher education institutions

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Transforming HR to better support higher education institutions

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Key Point: Human Resource Management, Strategic Management

Personal Abstract: This article examines the core challenges faced by university human resources departments in addressing institutional strategic objectives, such as enhancing teaching quality and workforce planning. Traditionally, the HR function in higher education has focused on compliance and administrative management, lacking strategic involvement. This results in limited effectiveness in talent attraction and development. The article proposes four specific improvement measures: First, require HR to co-develop strategies with university leadership, aligning human resources objectives with institutional goals. Second, establish clear professional pathways for HR personnel, spanning recruitment, training, and career development. Third, create cross-departmental “mobile HR teams” to flexibly deploy HR staff across campus operations. Fourth, continuously invest in HR services and technology, incrementally optimizing core processes. The ultimate goal is to transform HR from an administrative executor into a strategic enabler.

Personal analysis: After reading this article, I found that it effectively interprets the central idea of the course synopsis. It precisely corresponds to the three strategic planning and development stages in the course synopsis. Firstly, it analyzes the shortcomings of the traditional HR model through strategic positioning. Secondly, it determines the specific strategies for organizational development through strategic choices, such as using digital learning platforms to rebuild HR personnel and establishing an "agile configuration model" for the "mobile HR team". Finally, it realizes the flexible allocation of human resources during the implementation of the strategy.

Therefore, I believe that this article provides a very good case for a deeper understanding and learning of this course. Both university institutions and enterprises need to strategically promote the HR function to shift from routine administrative affairs to strategic talent management. At the same time, they should learn to critically reflect on the role of digital technology in the learning context and integrate digital technology to transform the organization into an innovative learning organization, in order to maintain sustainable competitive advantages.

Source: https://www.mckinsey.com/industries/education/our-insights/transforming-hr-to-better-support-higher-education-institutions#/

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Transforming HR to better support higher education institutions

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Higher ed institutions face stiff competition in the race for talent. A strong and connected HR function could give them an edge.

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Research on AIGC-Driven Personalized Mathematics Learning System

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Research on AIGC-Driven Personalized Mathematics Learning System

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Key Point:

AIGC Technology, Mathematics Learning, Tailored Learning, Education

Personal Abstract:

This article mainly discusses about AIGC-Driven personalized mathematics learning system research, explores how to use AI technology to impact students’ mathematics learning, reflects on current problems students have in learning mathematical concepts and theories, for instance, helps each student better understand mathematical concepts and theories by using AI technology to restore the historical scenes of the birth of mathematical concepts, dialogues and discussions between scholars, experience of mathematical experiments by themselves and situational teaching etc. Collecting student learning information by using AI technology, according to student different learning progress and study feedback to dynamic adjust, designing tailored learning plans to each student’s needs and providing personalized instruction. A controlled experiment was designed, with one group receiving traditional math instruction and the other receiving AI-based contextual instruction. Through math tests, pre- and post-learning assessments, questionnaires, and teacher feedback, the use of AI technology was verified to enhance students’ mathematical understanding and interest. In a word, this article demonstrates the feasibility and effectiveness of an AIGC-driven personalized mathematics learning system. Using AI technology can help students learn better.

Personal Analysis/Application:

In my view, technological advancements will gradually impact many industries, especially education, so I think it is indispensable part of integrating technology application to teaching environment in the future, of course, the current application of AI technology in education is still controversial. For instance, some AI will give me incorrect information, and affect my judgment, and some AI is currently very complex to operate etc., but there is still much to learn from the application of AI technology in education. A good example is this article, which applies AIGC technology to mathematics field by using AI technology to restore the historical scenes of the birth of mathematical concepts, dialogues and discussions between scholars, experience of mathematical experiments by themselves and situational teaching etc. The article also uses technology to build an information network for each student, dynamic assessment learning process of each students, help them to study, providing tailored learning based on different feedback from students to dynamic adjust study projects. In short, AI technology will definitely have an impact on the field of education in the future. How to effectively apply AI technology in teaching classrooms has triggered my thinking.

Reference: Wang, C. (2025). Research on AIGC-driven personalized mathematics learning system. SHS Web Conf., 215, 01003. https://doi.org/10.1051/shsconf/202521501003

Source：https://www.shs-conferences.org/articles/shsconf/abs/2025/06/shsconf_fems2025_01003/shsconf_fems2025_01003.html

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Research on AIGC-Driven Personalized Mathematics Learning System | SHS Web of Conferences

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SHS Web of Conferences, open access proceedings in Humanities and Social Sciences

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Sustainable Development Begins at School

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Sustainable Development Begins at School

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Key Point: Sustainability, Education

Personal Abstract:

The article emphasizes the critical role of universal education in achieving the UN Sustainable Development Goals (SDGs) by 2030. Despite global commitments, with just only five years left to achieve that pledge. It is impossible. There are at least 272 million children remain out of school, jeopardized by a lack of political will, insufficient funding, and systemic barriers like child labor, poverty, and conflict. The article highlights a $97 billion annual funding gap for education in low-income countries and notes declining aid from high-income nations. Progress in countries like Brazil and India shows that prioritizing education as a right improves outcomes. The author advocates for compassionate education systems that integrate empathy, experiential learning, and problem-solving to address injustices. Technology, including AI, can expand access, but human engagement remains essential. Compassion-driven education is key to creating responsible citizens for a sustainable future.

To sum up, sustainable development is a critical social issue. People must find solutions to address the challenges they face today while simultaneously cultivating children to tackle future problems. That's the value of education.

Reference:

Satyarthi, K. (2025, September, 15). Sustainable development begins at school. Project Syndicate. https://www.project-syndicate.org/magazine/what-it-will-take-to-achieve-sdg4-universal-education-by-kailash-satyarthi-2025-09

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Sustainable Development Begins at School

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Kailash Satyarthi considers what it will take to deliver on the promise of free, equitable, and quality education for all.

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‘Alarming’ slowdown in human development - could AI provide answers?

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‘Alarming’ slowdown in human development - could AI provide answers?

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Key Point: Humanities, Economic Development, AI

Personal Abstract:

This article is talking about the UN Development Programme’s (UNDP) 2025 Human Development Report highlights a concerning slowdown in global human development since the COVID-19 pandemic, with progress stalled across all regions. Inequalities between rich and poor countries have widened for four years, It is threatening the 2030 development milestone. However, the report is optimistic about artificial intelligence (AI) as a tool to enhance human development if guided by right policies. A survey shows that 60% of global respondents, and 70% in low- and medium-development countries, expect AI can boost productivity effectively and create more opportunities in education, health, and work. The report recommends modernizing education and health systems, fostering collaboration of human and AI, and ensuring equitable AI access through global governance. AI can be the potential solution to narrow the distance between wealthy and poverty countries. It can empower people to have their own small business.

In conclusion, global human development stalled is not a single country's responsibility. It is a global issue. By solving this problem may require the collaboration among countries. For instance, United States as a AI well-developed country. They can share AI techniques to low and medium developed countries to help them develop their own AI system, empowering their citizen with knowledge and skills. Additionally, it could provide economic and resources support. Help them to address the basic need of living.

Reference:

United Nations. (2025, September 9). ‘Alarming’ slowdown in human development - could AI provide answers?UN News.  https://news.un.org/en/story/2025/05/1162926

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For several decades, human development indicators showed a steady, upward curve and UN researchers predicted that by 2030, a high level of development would be enjoyed by the global population.

Those hopes have been dashed in recent years following a period of exceptional crises such as the COVID-19 pandemic - and progress has stalled across all regions of the world.

'Very real threat' to progress

The Human Development Report, an annual publication from the UN Development Programme ( UNDP), shows that inequalities between rich and poor countries have widened for the fourth year in a row.

Global pressures, such as increasing trade tensions and a worsening debt crisis which limits the ability of governments to invest in public services, are narrowing traditional paths to development.

"This deceleration signals a very real threat to global progress," said Achim Steiner, UNDP Administrator. "If 2024's sluggish progress becomes 'the new normal', that 2030 milestone could slip by decades - making our world less secure, more divided, and more vulnerable to economic and ecological shocks."

Maybe the robots aren't coming for our jobs after all

Despite the gloomy indicators, the report is strikingly upbeat about the potential of artificial intelligence, noting the breakneck pace at which free or low-cost tools have been embraced by businesses and individuals alike.

UNDP researchers carried out a survey to gauge opinions on AI and discovered that around 60 per cent of respondents expect the technology to positively impact their work and create new opportunities.

Those living in low and medium levels of development were particularly keen: 70 per cent expect AI to increase their productivity, and two thirds anticipate using AI in education, health, or work within the next year.

Action stations

The report's authors include recommendations for action to make sure that AI is as beneficial as possible, including the modernisation of education and health systems to adequately meet today's needs - building an economy focused on human collaboration with AI (rather than competition) - putting humans at the heart of AI development, from design to deployment.

"The choices we make in the coming years will define the legacy of this technological transition for human development," said Pedro Conceição, Director of UNDP's Human Development Report Office.

"With the right policies and focus on people, AI can be a crucial bridge to new knowledge, skills, and ideas that can empower everyone from farmers to small business owners."

Ultimately, the report's message is that the impact of AI is hard to predict. Rather than being an autonomous force, it is a reflection and amplifier of the values and inequalities of the societies that shape it.

To avoid what it calls "development disappointment", UNDP urges stronger global cooperation on AI governance, alignment between private innovation and public goals, and a renewed commitment to human dignity, equity, and sustainability.

"The 2025 HDR is not a report about technology," writes Mr. Steiner in the foreword. "It is a report about people - and our ability to reinvent ourselves in the face of profound change."

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'Alarming' slowdown in human development - could AI provide answers?

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The progress of human development - as measured by people's freedoms and well-being - has remained sluggish since the major shock of the COVID-19 pandemic. A UN study released on Tuesday finds that used in the right way, artificial intelligence could be a powerful tool to improve millions of lives.

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Optimizing Design Thinking Strategy for AI-Generated Image Models: Using Logo Design as a Case Study

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Optimizing Design Thinking Strategy for AI-Generated Image Models: Using Logo Design as a Case Study

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Key Point:

AI-Generated Image Models, Logo Design, Design Thinking Strategy, Strategic Management

Personal Abstract:

This article mainly discuss about optimizing design thinking strategy for AI-Generated Image technology, for example, using AI tools to generate logos, it describes the advantages and limitations of AI-Generated tools, it also proposes incorporating some design knowledge and theories, such as, left-right alignment, font size, spatial layout, into AI-Generated software. Allowing AI software to generate images based on these theoretical frameworks can greatly improve the previous randomness and unstable ,improve the quality and creativity of the generated images, allow the images to be used as much as possible, and generate high quality, aesthetically pleasing images based on the restrictions of prompt words. We fed high quality logo images into AI software, allowing it to achieve deep learning and summarize the logo’s characteristics and forms. This, combined with design theory, formed an AI design style. Through multi-dimensional evaluation, we found that the optimized AI logo design model significantly improved in creativity, aesthetics, and practicality. Finally, we added user feedback collection to the AI tool, allowing it to continuously optimize itself based on this feedback. In a word, AI-Generated image through integrate design thinking strategy can be optimized so that AI tools can become better and better in the future.

Personal Analysis/Application:

I personally believe that AI-Generated Image technology would break traditional design processes, and will be an indispensable component of the design field. Currently, through methods like prompts, large models, and image feeds, we can quickly and efficiently generate a batch of images, helping designers find inspiration, confirm design direction and style, reduce waste of time, materials, and costs, and improve production efficiency. At present, with the continuous advancement of technology, the incorporation of different design concepts and user feedback, and the continuous optimization of AI-Generated technology,  integrate different design theories and questions feedback by user, consistently optimized AI-Generated technology,

AI technology will soon become even more powerful. At the same time, the continuous innovation of AI technology will indeed replace some jobs and cause an unemployment crisis. However, AI technology is ultimately a tool, and I need to continue to learn, understand, and master AI tools so that I can better use them.

Reference: Chen, Y. (2025). Optimizing design thinking strategy for AI-generated image models: Using logo design as a case study. SHS Web Conf., 213, 02006. https://doi.org/10.1051/shsconf/202521302006

Source：https://www.shs-conferences.org/articles/shsconf/abs/2025/04/shsconf_messd2025_02006/shsconf_messd2025_02006.html

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Optimizing Design Thinking Strategy for AI-Generated Image Models: Using Logo Design as a Case Study | SHS Web of Conferences

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SHS Web of Conferences, open access proceedings in Humanities and Social Sciences