AI Research
How is AI shaping the future of education? A Baltimore tech founder weighs in.
Maryland education and tech professionals are weighing in as this Question Everything asks, “How is AI and new technology shaping the future of education?”
There is no generation that is more important for our future than the next one, and the future is changing by the minute, especially with technology.
The heavy focus on artificial intelligence has many parents wondering how it will impact the classroom.
AI in education
When it comes to education these days, it’s not what many of us remember as kids.
Who would have thought that kids would be learning virtually, using laptops and iPads instead of pencils and paper, and also experimenting with artificial intelligence?
“I would say AI is coming whether you like it or not. That’s the most direct way I can say it. The best thing we can do is get in front of it,” said Brandon Phillips, Future Think Hub Founder, Creator of A.I. Software.
Seventh-grader De’Aria Johnson is embracing every second of it.
“It’s a new thing to me, but it’s super fun and cool,” said Johnson, a student at City Springs Elementary School.
AI programs in schools
WJZ got an inside look at City Springs’ Future Think Edge summer program. The pilot program includes trying out new AI software that was developed by Phillips.
“It’s an AI software that gives each student an individual teacher,” Phillips said. “That software learns that student and teaches that student at their capacity.”
The software caters to the personal needs of every student to teach them subjects like math, science and coding in a game-like setting.
“It’ll engage with you almost like a human being. It’s a computer, but it’s going to learn you and build your profile around you,” said Phillips.
It helps students solve problems and think critically, but even more importantly, it maximizes their learning potential.
“I realized by paying attention in school and also being a student that was gifted, that the gifted students get attention,” Phillips said. “Even if I was a special needs student or a gifted student, I would still get the same amount of attention.”
“I would love to use this during school because if I don’t know something that the teacher tells me, I can just go on Future Think and they can like explain it more,” said Johnson. “I can ask about the question instead of just asking my teacher.”
School leaders react
City Springs Elementary School Assistant Principal Rob Summers sees the importance of having students engage with technologies like this that could soon dominate our future.
“We can’t just be preparing our kids for the economy of five years ago or the economy today; we have to be ready for what’s next,” said Sumers.
“The teacher’s still there. The teacher still has their own lesson plan and they push that student, but the computer is assisting them one-by-one to make sure they get the best outcome,” Phillips said.
It’s a front row seat on how AI and new technology are shaping the future of education. It’s even shaping how we do interviews.
We sat down virtually with Dr. Aileen Hawkins, CEO of Inspired Online Schools USA.
She feels her school is ahead of the game as well.
We asked Dr. Hawkins about some of the benefits that students can look forward to at her school.
“The flexibility that online schooling provides is just second to none,” said Dr. Hawkins. “The ability for students to engage with their learning wherever, whenever they are, and the ability to take school with them when they’re on the go is just, it’s fantastic.”
Enrollment for the school exploded beyond what started as mostly student-athletes.
It picked up steam post-pandemic, and the school now offers live classes in the U.S. and enrolls students all year round.
“The growth in online schooling says to me that parents are looking for alternatives for their children,” Dr. Hawkins said. “They know that their children deserve an education that’s built expressly for them, and an online school can do that in ways that brick and mortar schools aren’t quite yet set up for. So the future is already here, in my opinion.”
At Inspired Online Schools USA, students can interact with classmates all around the world and use augmented reality for lessons, allowing them to step inside a museum or learn about the human heart in a virtual lab.
When asked if she thinks every school will have to catch up to this technology and artificial intelligence, Dr. Hawkins said. “Absolutely.”
“In the same way that we all had to wrap our brains around using a calculator to do math when we were all in school, now we have to embrace these technologies because they are essential to the jobs of the future and most importantly, they accelerate students along their own learning pathways and to be able to reason critically, Dr. Hawkins said. “To be able to analyze, to be able to create. Those are higher-order thinking skills and competencies that students today must master in order to be competitive in the workforce tomorrow.”
It’s uncertain what the future holds, but we have a glimpse. Covered books, chalkboards and other classrooms could soon be history.
Educators say don’t resist, embrace it.
“If we try to just bury our heads in the sand, the outcomes are not going to be helpful to us,” said Assistant Principal Summers.
“We have an opportunity to jump in front and take control of it, or we can let it take control of us,” said Phillips.
Despite the growing trend of artificial intelligence, a new survey by Junior Achievement shows that more than half of teens, 64%, report that their schools or teachers do not encourage the use of AI as learning tools.
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A few minutes after losing to Jannik Sinner at the US Open 2025 with a convincing score against him, Alexander Bublik reacted on social media to the incredible performance of the world number one. The Kazakh player commented on a picture with the result: “AI,” once again referring to the Italian as Artificial Intelligence, always as a compliment to his amazing level on the court.
And post-match he was very quick to insist on his point. https://t.co/qjuPPcHxif pic.twitter.com/7B1rhCUWUH
— José Morgado (@josemorgado) September 2, 2025
This news is an automatic translation. You can read the original news, Bublik reacciona en redes sociales tras perder contra Sinner: “Es Inteligencia Artificial”
Artificial Intelligence (AI) could help Indonesia achieve its vision of Golden Indonesia 2045 with the right strategy and governance, according to Minister of Communication and Digital Affairs, Meutya Hafid.
Stating this in her forward to Indonesia’s National AI Roadmap White Paper, she said the AI roadmap would provide policy direction to accelerate AI ecosystem development to ensure the country was not to be left behind in a field increasingly dominated by advanced countries and global tech giants.
The White Paper, drafted by the AI Roadmap Task Force, a 443-member body representing government, academia, industry, civil society, and the media, was launched by the Ministry of Communication and Digital in early August.
It has been envisaged as a strategic document that would serve as the country’s reference for adopting and developing AI technology in a more focused, inclusive, and ethical manner. The document has been circulated for public consultation to gather wider input from stakeholders.
This initiative builds on the National AI Strategy 2020-2045, which was an initial framework developed by the Collaborative Research and Industrial Innovation in AI (KORIKA), an organisation formed by scientists, technocrats and industry leaders to accelerate the AI ecosystem in Indonesia.
However, that strategy has struggled to keep up with the rapid breakthroughs in generative AI (GenAI) since late 2022.
Three major action plans
The national AI roadmap outlines three main action plans: AI ecosystems, AI development priorities, and AI financing – all anchored in ethical guidance and regulation.
This roadmap also breaks down the action plan into three-time horizons: short term (2025-2027), medium term (2028-2035) and long term (2035-2045).
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Indonesia’s AI ecosystem development would focus on three main pillars.
The first pillar was talent development.
Indonesia aimed to nurture a large pool of skilled professionals who could both use and create AI innovation.
The roadmap sets an ambitious target of producing 100,000 AI talents annually. Around 30 per cent would be developers, divided further into AI specialists (30 per cent) and practitioners (70 per cent), and the remaining 70 per cent would be AI end-users.
The government also aimed to ensure 20 million citizens are AI-literate by 2029.
The next pillar was research and industrial innovation.
The roadmap emphasised advanced, relevant, and sustainable AI research that delivered real benefits to society.
To achieve this, the government would encourage agencies, universities, and industries to strengthen AI programmes in priority sectors.
A cross-sectoral open sandbox platform would also be developed to support experimentation and collaboration.
The last pillar in Indonesia’s AI ecosystem was infrastructure and data.
To foster domestic AI innovation, the government planned to expand digital infrastructure, including high-performance computing, GPUs/TPUs, and a national cloud hosted in sovereign data centres to ensure secure and regulated data management.
The white paper also outlined plans to promote the development of green data centres through public–private partnerships.
Strategic priorities in AI development
The roadmap focuses on developing AI for strategic use cases, ensuring that AI adoption delivers meaningful and sustainable impact.
These priorities closely align with the country’s national development agenda and President Prabowo’s Asta Cita vision.
The priority sectors for AI include food security, healthcare, education, economy and finance, bureaucratic reform, politics and security, energy, environment, housing, transport and logistics, as well as arts, culture, and the creative economy.
Public services were also identified as an immediate priority for the 2025–2027 term. In healthcare, AI would be applied for early disease detection, remote patient monitoring, and optimising the distribution of medicines and vaccines.
In education, the focus would be on adaptive learning and digital platforms for personalised teaching materials. The government also plans to develop automated evaluation systems to ease assessment processes in schools.
In governance, AI applications would centre on intelligent chatbots for public services and data-driven policy analytics.
For transport and mobility, development would be directed towards smart traffic systems, public transport management, and the optimisation of national logistics.
Financing the national AI agenda
The roadmap outlined a phased financing strategy, combining state budget allocations, private sector contributions, and external partnerships through bilateral and multilateral collaborations.
Over the next two decades, the government aimed to establish a sustainable financing ecosystem driven by industry participation and international investment. To achieve this, Indonesia will expand fiscal incentives to encourage AI-related investments.
A notable feature of the roadmap was the role of Danantara, Indonesia’s newly established sovereign wealth fund, which has been tasked with spearheading AI financing.
Danantara would design innovative financial instruments, establish a Sovereign AI Fund, and develop blended financing models for the country’s strategic AI projects.
In the initial phase, financing would target fundamental research, pilot projects in the public sector, and the development of data and computing infrastructure.
Subsequent stages would extend funding to industries, research institutions, universities, and domestic AI start-ups, with the goal of strengthening Indonesia’s AI ecosystem and boosting its global competitiveness.
AI Research
MAIA platform for routine clinical testing: an artificial intelligence embryo selection tool developed to assist embryologists
Graham, M. E. et al. Assisted reproductive technology: Short- and long-term outcomes. Dev. Med. Child. Neurol. 65, 38–49 (2023).
Jiang, V. S. & Bormann, C. L. Artificial intelligence in the in vitro fertilization laboratory: a review of advancements over the last decade. Fertil. Steril. 120, 17–23 (2023).
Devine, K. et al. Single vitrified blastocyst transfer maximizes liveborn children per embryo while minimizing preterm birth. Fertil. Steril. 103, 1454–1460 (2015).
Tiitinen, A. Single embryo transfer: why and how to identify the embryo with the best developmental potential. Best Pract. Res. Clin. Endocrinol. Metab. 33, 77–88 (2019).
Glatstein, I., Chavez-Badiola, A. & Curchoe, C. L. New frontiers in embryo selection. J. Assist. Reprod. Genet. 40, 223–234 (2023).
Gardner, D. K. & Schoolcraft, W. B. Culture and transfer of human blastocysts. Curr. Opin. Obstet. Gynaecol. 11, 307–311 (1999).
Sciorio, R. & Meseguer, M. Focus on time-lapse analysis: blastocyst collapse and morphometric assessment as new features of embryo viability. Reprod. BioMed. Online. 43, 821–832 (2021).
Sundvall, L., Ingerslev, H. J., Knudsen, U. B. & Kirkegaard, K. Inter- and intra-observer variability of time-lapse annotations. Hum. Reprod. 28, 3215–3221 (2013).
Gallego, R. D., Remohí, J. & Meseguer, M. Time-lapse imaging: the state of the Art. Biol. Reprod. 101, 1146–1154 (2019).
VerMilyea, M. D. et al. Computer-automated time-lapse analysis results correlate with embryo implantation and clinical pregnancy: a blinded, multi-centre study. Reprod. Biomed. Online. 29, 729–736 (2014).
Chéles, D. S., Molin, E. A. D., Rocha, J. C. & Nogueira, M. F. G. Mining of variables from embryo morphokinetics, blastocyst’s morphology and patient parameters: an approach to predict the live birth in the assisted reproduction service. JBRA Assist. Reprod. 24, 470–479 (2020).
Rocha, C., Nogueira, M. G., Zaninovic, N. & Hickman, C. Is AI assessment of morphokinetic data and digital image analysis from time-lapse culture predictive of implantation potential of human embryos? Fertil. Steril. 110, e373 (2018).
Zaninovic, N. et al. Application of artificial intelligence technology to increase the efficacy of embryo selection and prediction of live birth using human blastocysts cultured in a time-lapse incubator. Fertil. Steril. 110, e372–e373 (2018).
Alegre, L. et al. First application of artificial neuronal networks for human live birth prediction on Geri time-lapse monitoring system blastocyst images. Fertil. Steril. 114, e140 (2020).
Bori, L. et al. An artificial intelligence model based on the proteomic profile of euploid embryos and blastocyst morphology: a preliminary study. Reprod. BioMed. Online. 42, 340–350 (2021).
Chéles, D. S. et al. An image processing protocol to extract variables predictive of human embryo fitness for assisted reproduction. Appl. Sci. 12, 3531 (2022).
Jacobs, C. K. et al. Embryologists versus artificial intelligence: predicting clinical pregnancy out of a transferred embryo who performs it better? Fertil. Steril. 118, e81–e82 (2022).
Lorenzon, A. et al. P-211 development of an artificial intelligence software with consistent laboratory data from a single IVF center: performance of a new interface to predict clinical pregnancy. Hum. Reprod. 39, deae108.581 (2024).
Fernandez, E. I. et al. Artificial intelligence in the IVF laboratory: overview through the application of different types of algorithms for the classification of reproductive data. J. Assist. Reprod. Genet. 37, 2359–2376 (2020).
Mendizabal-Ruiz, G. et al. Computer software (SiD) assisted real-time single sperm selection associated with fertilization and blastocyst formation. Reprod. BioMed. Online. 45, 703–711 (2022).
Fjeldstad, J. et al. Segmentation of mature human oocytes provides interpretable and improved blastocyst outcome predictions by a machine learning model. Sci. Rep. 14, 10569 (2024).
Khosravi, P. et al. Deep learning enables robust assessment and selection of human blastocysts after in vitro fertilization. NPJ Digit. Med. 2, 21 (2019).
Hickman, C. et al. Inner cell mass surface area automatically detected using Chloe eq™(fairtility), an ai-based embryology support tool, is associated with embryo grading, embryo ranking, ploidy and live birth outcome. Fertil. Steril. 118, e79 (2022).
Tran, D., Cooke, S., Illingworth, P. J. & Gardner, D. K. Deep learning as a predictive tool for fetal heart pregnancy following time-lapse incubation and blastocyst transfer. Hum. Reprod. 34, 1011–1018 (2019).
Rajendran, S. et al. Automatic ploidy prediction and quality assessment of human blastocysts using time-lapse imaging. Nat. Commun. 15, 7756 (2024).
Bormann, C. L. et al. Consistency and objectivity of automated embryo assessments using deep neural networks. Fertil. Steril. 113, 781–787e1 (2020).
Kragh, M. F. & Karstoft, H. Embryo selection with artificial intelligence: how to evaluate and compare methods? J. Assist. Reprod. Genet. 38, 1675–1689 (2021).
Cromack, S. C., Lew, A. M., Bazzetta, S. E., Xu, S. & Walter, J. R. The perception of artificial intelligence and infertility care among patients undergoing fertility treatment. J. Assist. Reprod. Genet. https://doi.org/10.1007/s10815-024-03382-5 (2025).
Fröhlich, H. et al. From hype to reality: data science enabling personalized medicine. BMC Med. 16, 150 (2018).
Zhu, J. et al. External validation of a model for selecting day 3 embryos for transfer based upon deep learning and time-lapse imaging. Reprod. BioMed. Online. 47, 103242 (2023).
Yelke, H. K. et al. O-007 Simplifying the complexity of time-lapse decisions with AI: CHLOE (Fairtility) can automatically annotate morphokinetics and predict blastulation (at 30hpi), pregnancy and ongoing clinical pregnancy. Hum. Reprod. 37, deac104.007 (2022).
Papatheodorou, A. et al. Clinical and practical validation of an end-to-end artificial intelligence (AI)-driven fertility management platform in a real-world clinical setting. Reprod. BioMed. Online. 45, e44–e45 (2022).
Salih, M. et al. Embryo selection through artificial intelligence versus embryologists: a systematic review. Hum. Reprod. Open hoad031 (2023).
Nunes, K. et al. Admixture’s impact on Brazilian population evolution and health. Science. 388(6748), eadl3564 (2025).
Jackson-Bey, T. et al. Systematic review of Racial and ethnic disparities in reproductive endocrinology and infertility: where do we stand today? F&S Reviews. 2, 169–188 (2021).
Kassi, L. A. et al. Body mass index, not race, May be associated with an alteration in early embryo morphokinetics during in vitro fertilization. J. Assist. Reprod. Genet. 38, 3091–3098 (2021).
Pena, S. D. J., Bastos-Rodrigues, L., Pimenta, J. R. & Bydlowski, S. P. DNA tests probe the genomic ancestry of Brazilians. Braz J. Med. Biol. Res. 42, 870–876 (2009).
Fraga, A. M. et al. Establishment of a Brazilian line of human embryonic stem cells in defined medium: implications for cell therapy in an ethnically diverse population. Cell. Transpl. 20, 431–440 (2011).
Amin, F. & Mahmoud, M. Confusion matrix in binary classification problems: a step-by-step tutorial. J. Eng. Res. 6, 0–0 (2022).
Magdi, Y. et al. Effect of embryo selection based morphokinetics on IVF/ICSI outcomes: evidence from a systematic review and meta-analysis of randomized controlled trials. Arch. Gynecol. Obstet. 300, 1479–1490 (2019).
Guo, Y. H., Liu, Y., Qi, L., Song, W. Y. & Jin, H. X. Can time-lapse incubation and monitoring be beneficial to assisted reproduction technology outcomes? A randomized controlled trial using day 3 double embryo transfer. Front. Physiol. 12, 794601 (2022).
Giménez, C., Conversa, L., Murria, L. & Meseguer, M. Time-lapse imaging: morphokinetic analysis of in vitro fertilization outcomes. Fertil. Steril. 120, 228–227 (2023).
Vitrolife EmbryoScope + time-lapse system. (2023). https://www.vitrolife.com/products/time-lapse-systems/embryoscopeplus-time-lapse-system/.
Lagalla, C. et al. A quantitative approach to blastocyst quality evaluation: morphometric analysis and related IVF outcomes. J. Assist. Reprod. Genet. 32, 705–712 (2015).
Rocha, J. C. et al. A method based on artificial intelligence to fully automatize the evaluation of bovine blastocyst images. Sci. Rep. 7, 7659 (2017).
Chavez-Badiola, A. et al. Predicting pregnancy test results after embryo transfer by image feature extraction and analysis using machine learning. Sci. Rep. 10, 4394 (2020).
Matos, F. D., Rocha, J. C. & Nogueira, M. F. G. A method using artificial neural networks to morphologically assess mouse blastocyst quality. J. Anim. Sci. Technol. 56, 15 (2014).
Wang, S., Zhou, C., Zhang, D., Chen, L. & Sun, H. A deep learning framework design for automatic blastocyst evaluation with multifocal images. IEEE Access. 9, 18927–18934 (2021).
Berntsen, J., Rimestad, J., Lassen, J. T., Tran, D. & Kragh, M. F. Robust and generalizable embryo selection based on artificial intelligence and time-lapse image sequences. PLoS One. 17, e0262661 (2022).
Fruchter-Goldmeier, Y. et al. An artificial intelligence algorithm for automated blastocyst morphometric parameters demonstrates a positive association with implantation potential. Sci. Rep. 13, 14617 (2023).
Illingworth, P. J. et al. Deep learning versus manual morphology-based embryo selection in IVF: a randomized, double-blind noninferiority trial. Nat. Med. 30, 3114–3120 (2024).
Kanakasabapathy, M. K. et al. Development and evaluation of inexpensive automated deep learning-based imaging systems for embryology. Lab. Chip. 19, 4139–4145 (2019).
Loewke, K. et al. Characterization of an artificial intelligence model for ranking static images of blastocyst stage embryos. Fertil. Steril. 117, 528–535 (2022).
Hengstschläger, M. Artificial intelligence as a door opener for a new era of human reproduction. Hum. Reprod. Open hoad043 (2023).
Lassen Theilgaard, J., Fly Kragh, M., Rimestad, J., Nygård Johansen, M. & Berntsen, J. Development and validation of deep learning based embryo selection across multiple days of transfer. Sci. Rep. 13 (1), 4235 (2023).
Lozano, M. et al. P-301 Assessment of ongoing clinical outcomes prediction of an AI system on retrospective SET data, Human Reprod. 38(Issue Supplement_1), dead093.659. (2023).
Collins, G. S. et al. TRIPOD + AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods. BMJ 385, e078378 (2024).
Abdolrasol, M. G. M. et al. Artificial neural networks based optimization techniques: a review. Electronics 10, 2689 (2021).
Yuzer, E. O. & Bozkurt, A. Instant solar irradiation forecasting for solar power plants using different ANN algorithms and network models. Electr. Eng. 106, 3671–3689 (2024).
Guariso, G. & Sangiorgio, M. Improving the performance of multiobjective genetic algorithms: an elitism-based approach. Information 11, 587 (2020).
García-Pascual, C. M. et al. Optimized NGS approach for detection of aneuploidies and mosaicism in PGT-A and imbalances in PGT-SR. Genes 11, 724 (2020).
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