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Artificial intelligence goes to school – Opinion

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MA XUEJING/CHINA DAILY

Artificial intelligence is no longer a distant concept from science fiction. It has become a force that can reshape industries, societies, and the very essence of human interaction. Education, the cornerstone of social progress, is increasingly becoming intertwined with AI. The way countries equip the next generation with AI skills could determine their technological edge in the future. In this area, China is quietly taking the lead.

While Western countries, especially the United States, have grabbed headlines for AI breakthroughs, their approach to K-12 (kindergarten through 12th grade) AI education is cautious, fragmented and largely pilot-driven. China”s approach, in contrast, is a methodical, policy-driven blueprint, that lays a solid foundation for AI literacy from primary school level upward. Across the country, AI-themed summer camps and integrated curriculums are emerging, forming a burgeoning AI education ecosystem which, beyond coding, is nurturing broader cognitive skills and ethical reasoning.

China’s rapid progress in AI education is no accident; it is the result of careful top-level planning, and coordinated action. National and local authorities have worked together to ensure that AI education is not just a novelty, but a structured part of students’ learning journey. Schools across China now include AI courses as a regular part of the curriculum; teachers receive special training in AI and digital learning platforms support AI teaching in class. From bustling cities to smaller towns, this nationwide push has created a well-organized framework, ensuring every student gets a chance to learn foundational AI.

However, policy alone is not enough. China has established a collaborative mechanism involving enterprises, universities and research institutions. It integrates leading tech companies in codesigning curriculums, training teachers and building AI learning platforms. iFlytek’s AI textbook, co-developed with Northwest Normal University, is now used across the country, reaching schools and students nationwide. This three-tiered approach — central policy, local execution and broad social participation — ensures AI education is not only conceptual but also operational.

China’s AI education policy avoids reducing students to code-crunching machines. Instead, it embeds AI across traditional subjects such as the Chinese language, art, and comprehensive practice courses, creating a “discipline integration and technology empowerment” model. In many schools, art classes now incorporate AI image-generation tools, encouraging students to co-create with machines. In other areas, generative AI is integrated into writing courses, prompting students to critique, revise, and re-imagine AI-generated text. AI education is as much about reshaping cognitive and expressive abilities as it is about teaching technical skills.

Competitions further promote applied learning. Contests like the National Olympiad in Informatics and the China Adolescents Science and Technology Innovation Contest now include AI modules, giving students hands-on experience with algorithms and machine thinking. Tsinghua University’s Qiuzhen College, for example, offers a youth mathematics and AI summer camp, blending AI fundamentals, Python programming, mathematical modeling and ethical reflection. Such programs create a smooth transition “from classroom knowledge to real-world project practice”, moving students from passive understanding to active problem-solving.

China’s AI education path contrasts with that of the US. While the US excels in AI research, its K-12 implementation is decentralized and uneven, largely dependent on local initiatives or partnerships with higher education institutions.

Besides, AI education in the US is promoted often through extracurricular clubs, summer camps or online courses. Yes, it is flexible and innovative, but it is also inconsistent in coverage, continuity and scale. Also, heightened concerns over ethics, safety and privacy sometimes restrict classroom usage of generative AI tools, creating a “tech enthusiasm, educational hesitation” paradox.

For China, on the other hand, AI literacy is about developing foundational competency, akin to reading, writing and arithmetic. Students are taught to view AI not only as a set of tools, but also as a digital language and medium for critical thinking, creativity and ethical awareness. In short, China’s approach is “institution-driven and universal”, aimed at ensuring every child develops core capabilities, while the US’ approach is “market-driven and selective”, letting individual interests determine engagement. Both paths have merits, but in a rapidly evolving AI landscape, early-structured cultivation of cognitive frameworks may prove decisive when it comes to nurturing the next generation of tech talents.

No system is perfect. For example, some Chinese schools still focus narrowly on tools, with shallow, homogenized curricula. Teachers’ expertise varies, and evaluation methods to measure AI literacy levels are not yet fully developed. The bigger challenge is to prevent AI education from becoming a new form of exam-oriented competition.

How can students learn technical skills while cultivating ethical judgment, social responsibility and humanistic sensitivity? Some schools are already exploring AI ethics, algorithmic bias and socially impactful subjects, encouraging reflective thinking alongside technical mastery. To truly become AI literate, one needs to have technical understanding, collaborative capability and value-based judgment — a human-centered rather than purely technocratic approach.

China’s push to integrate AI into foundational education echoes Deng Xiaoping’s statement in 1984 in which he emphasized that “computer literacy should start with children”. From top-level design to local execution, from curriculum reform to competitions, and from technical training to ethical literacy, China’s AI education path is structural and gradual — a “slow-cooked” approach that builds deep roots rather than chasing short-term headlines.

In the era of global restructuring of education and rapid digital transformation, China is first reconstructing its young citizens’ cognitive abilities and problem-solving skills in order to gain a decisive advantage. In this high-stakes race for talent, China has quietly moved ahead.

The author is professor at Faculty of Education, East China Normal University. The views don’t necessarily represent those of China Daily.

If you have a specific expertise, or would like to share your thought about our stories, then send us your writings at opinion@chinadaily.com.cn, and comment@chinadaily.com.cn.

 

 



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Love and Artificial Intelligence – cbsnews.com

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Love and Artificial Intelligence  cbsnews.com



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NFL player props, odds, lines: Week 2, 2025 NFL picks, SportsLine Machine Learning Model AI predictions, SGP

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The Under went 12-4 in Week 1, indicating that not only were there fewer points scored than expected, but there were also fewer yards gained. Backing the Under with NFL prop bets was likely profitable for the opening slate of games, but will that maintain with Week 2 NFL props? Interestingly though, four of the five highest-scoring games last week were the primetime games, so if that holds, then the Overs for this week’s night games could be attractive with Week 2 NFL player props.

There’s a Monday Night Football doubleheader featuring star quarterbacks like Baker Mayfield, C.J. Stroud and Justin Herbert. The games also feature promising rookies such as Ashton Jeanty, Omarion Hampton and Emeka Egbuka. Prop lines are usually all over the place early in the season as sportsbooks attempt to establish a player’s potential, and you could take advantage of this with the right NFL picks. If you are looking for NFL prop bets or NFL parlays for Week 2, SportsLine has you covered with the top Week 2 player props from its Machine Learning Model AI.

Built using cutting-edge artificial intelligence and machine learning techniques by SportsLine’s Data Science team, AI Predictions and AI Ratings are generated for each player prop. 

Now, with the Week 2 NFL schedule quickly approaching, SportsLine’s Machine Learning Model AI has identified the top NFL props from the biggest Week 2 games.

Week 2 NFL props for Sunday’s main slate

After analyzing the NFL props from Sunday’s main slate and examining the dozens of NFL player prop markets, the SportsLine’s Machine Learning Model AI says Lions receiver Amon-Ra St. Brown goes Over 63.5 receiving yards (-114) versus the Bears at 1 p.m. ET. Detroit will host this contest, which is notable as St. Brown has averaged 114 receiving yards over his last six home games. He had at least 70 receiving yards in both matchups versus the Bears a year ago.

Chicago allowed 12 receivers to go Over 63.5 receiving yards last season as the Bears’ pass defense is adept at keeping opponents out of the endzone but not as good at preventing yardage. Chicago allowed the highest yards per attempt and second-highest yards per completion in 2024. While St. Brown had just 45 yards in the opener, the last time he was held under 50 receiving yards, he then had 193 yards the following week. The SportsLine Machine Learning Model projects 82.5 yards for St. Brown in a 4.5-star pick. See more Week 2 NFL props here.

Week 2 NFL props for Vikings vs. Falcons on Sunday Night Football

After analyzing Falcons vs. Vikings props and examining the dozens of NFL player prop markets, the SportsLine’s Machine Learning Model AI says Falcons running back Bijan Robinson goes Over 65.5 rushing yards (-114). Robinson ran for 92 yards and a touchdown in Week 14 of last season versus Minnesota, despite the Vikings having the league’s No. 2 run defense a year ago. The SportsLine Machine Learning Model projects Robinson to have 81.8 yards on average in a 4.5-star prop pick. See more NFL props for Vikings vs. Falcons here

You can make NFL prop bets on Robinson, Justin Jefferson and others with the Underdog Fantasy promo code CBSSPORTS2. Pick at Underdog Fantasy and get $50 in bonus funds after making a $5 wager:

Week 2 NFL props for Buccaneers vs. Texans on Monday Night Football

After analyzing Texans vs. Buccaneers props and examining the dozens of NFL player prop markets, the SportsLine’s Machine Learning Model AI says Bucs quarterback Baker Mayfield goes Under 235.5 passing yards (-114). While Houston has questions regarding its offense, there’s little worry about the team’s pass defense. In 2024, Houston had the second-most interceptions, the fourth-most sacks and allowed the fourth-worst passer rating. Since the start of last year, and including the playoffs, the Texans have held opposing QBs under 235.5 yards in 13 of 20 games. The SportsLine Machine Learning Model forecasts Mayfield to finish with just 200.1 passing yards, making the Under a 4-star NFL prop. See more NFL props for Buccaneers vs. Texans here

You can also use the latest FanDuel promo code to get $300 in bonus bets instantly:

Week 2 NFL props for Chargers vs. Raiders on Monday Night Football

After analyzing Raiders vs. Chargers props and examining the dozens of NFL player prop markets, the SportsLine’s Machine Learning Model AI says Chargers quarterback Justin Herbert goes Under 254.5 passing yards (-114). The Raiders’ defense was underrated in preventing big passing plays a year ago as it ranked third in the NFL in average depth of target allowed. It forced QBs to dink and dunk their way down the field, which doesn’t lead to big passing yardages, and L.A. generally prefers to not throw the ball anyway. Just four teams attempted fewer passes last season than the Chargers, and with L.A. running for 156.5 yards versus Vegas last season, Herbert shouldn’t be overly active on Monday night. He’s forecasted to have 221.1 passing yards in a 4.5-star NFL prop bet. See more NFL props for Chargers vs. Raiders here

How to make Week 2 NFL prop picks

SportsLine’s Machine Learning Model has identified another star who sails past his total and has dozens of NFL props rated 4 stars or better. You need to see the Machine Learning Model analysis before making any Week 2 NFL prop bets.

Which NFL prop picks should you target for Week 2, and which quarterback has multiple 5-star rated picks? Visit SportsLine to see the latest NFL player props from SportsLine’s Machine Learning Model that uses cutting-edge artificial intelligence to make its projections.





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What Is One of the Best Artificial Intelligence (AI) Stocks to Buy Now? – The Motley Fool

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What Is One of the Best Artificial Intelligence (AI) Stocks to Buy Now?  The Motley Fool



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