Short, useful summaries of coding, AI, and education news, with a practical next step for learners.
A 31-year-old graphic designer began with HTML and CSS, then used those skills to make custom WordPress changes. She continued with a front-end program to expand what she could do in her existing role rather than treating coding as an all-or-nothing career switch.
This quiz explores how to work with Excel files in Python using openpyxl. It covers reading and writing cell values, using formulas, and adding styling, charts, and images.
This learning path introduces FastAPI from the basics. It covers building REST APIs, serving web pages with Jinja2 templates, and creating a complete URL shortener project in Python.
The excerpt argues that simplifying texts for students can weaken the reading experience and reduce the effortful thinking that helps them learn. It says some schools once praised an AI tool for leveling text, but the author warns that this shortcut can do the thinking for students instead of helping them access complex texts. It also notes that many states are moving away from text-leveling programs.
This article introduces Python’s built-in functions for working with math, data types, iterables, and input/output. It also explains when to use each one to write code that feels more natural in Python.
This quiz checks your knowledge of Python’s built-in functions for math, data types, iterables, and input/output. It also helps you learn when to use each function.
The excerpt says AI is becoming a business capability across many sectors, but many organisations still struggle to move beyond experiments. It argues that success depends less on technical tools and more on an AI mindset: curiosity, critical thinking, strategic use, responsible implementation, and change management.
This article explains how NumPy reshape() changes the shape of an array in Python. It shows how to add or remove dimensions and how the data is rearranged when you reshape it.
Khan Academy argues that a correct answer alone does not show how a student understands a problem. Explaining, revising, and reasoning make that thinking visible, which supports more useful feedback and timely intervention. AI should support this process rather than replace it.
This quiz practices NumPy's reshape() method for changing an array's dimensions. It covers adding or removing dimensions and controlling how the values are rearranged.
UNESCO and LG launched a free, nine-hour intermediate AI ethics course on Coursera. It is available in 11 languages, requires no advanced technical knowledge, and uses hands-on projects to examine privacy, transparency, sustainability, and human autonomy.
Google and the University of Waterloo’s Futures Lab is helping students create practical AI tools. The program also aims to prepare students for future jobs by giving them real-world experience.
Teachers see AI as a way to fill subject and language gaps for bilingual students. Researchers are exploring chatbots and multilingual models that can adapt learning materials, support parent-child dialogue, and offer explanations in a child’s home language while keeping core content in English. Experts say the approach could expand access if used well.
Sal Khan says the first version of Khanmigo did not improve learning as the team had hoped. Later iterations placed AI inside practice, prompted students to explain their thinking, avoided simply giving answers, and aimed to multiply teachers’ capacity.
Elizabeth Kennard moved from archaeology into an application support role after completing a coding bootcamp. Full-stack fundamentals, agile teamwork, hackathons, and real projects helped her build experience without a previous technical career.
An Indigenous school’s technology coordinator describes how AI changed teachers’ work from routine support to urgent problem-solving. Teachers felt panic and embarrassment when student writing turned out to be AI-generated, leaving students to succeed without much learning. These incidents showed a broader policy gap the school had to address.
Microsoft’s third AI in Education Report surveyed over 3,000 students, educators, and leaders in six countries. The discussion highlights that while many people have tried AI tools, daily use is still much lower, student optimism has dipped, and leaders and teachers disagree about how much training has happened. It also suggests the cheating concern reflects a broader academic integrity issue, not just AI.