The growing enrollments in computer science courses and increase in class sizes necessitate scalable, automated tutoring solutions to adequately support student learning. While Large Language Models (LLMs) like GPT-3.5 have demonstrated potential in assisting students through question-answering, educators have significant concerns about students misusing LLMs or LLMs misleading students with inaccurate answers. This paper introduces CourseAssist, a novel LLM-based tutoring system tailored for computer science education. Unlike generic LLM systems, CourseAssist leverages retrieval-augmented generation along with user intent classification and post-processing to ensure that responses align with specific course learning goals, thereby addressing the pedagogical appropriateness of LLMs in educational settings. I evaluate CourseAssist against a baseline of GPT 3.5 using a dataset of 50 question-answer pairs from a programming languages course, focusing on the criteria of usefulness, accuracy, and pedagogical appropriateness. Evaluation results show that CourseAssist significantly outperforms the baseline, demonstrating its potential to serve as an effective learning assistant. This work not only highlights the importance of deliberate design considerations in LLM-based educational tools but also opens up avenues for future research, particularly in understanding user interactions with such systems in real-world scenarios and integrating human educators into LLM-based tutoring systems.

ChatGPT has proven it can help students with their homework, but now it is helping teachers create those very courses, a computer science professor told Fox News. As educators debate whether students should be allowed to use artificial intelligence for assignments, one professor told Fox News that teachers themselves are using the tech to help with their lessons. "I know faculty who are using ChatGPT to help write syllabi and to write exams," a University of California, Berkeley professor of computer science, Hany Farid, told Fox News. "I've seen professors using it to help design courses, write exam problems, write homework problems." "It is both an enabling and a potentially problematic technology," he continued.

This past spring, Stanford University computer scientists unveiled their pandemic brainchild, Code In Place, a project where 1,000 volunteer teachers taught 10,000 students across the globe the content of an introductory Stanford computer science course. Students in Code In Place evaluated the feedback they received using this carefully designed user interface. While the instructors could share their knowledge with hundreds, even thousands, of students at a time during lectures, when it came to homework, large-scale and high-quality feedback on student assignments seemed like an insurmountable task. "It was a free class anyone in the world could take, and we got a whole bunch of humans to help us teach it," said Chris Piech, assistant professor of computer science and co-creator of Code In Place. "But the one thing we couldn't really do is scale the feedback. To solve this problem, Piech worked with Chelsea Finn, assistant professor of computer science and of electrical engineering, and PhD students Mike Wu and Alan Cheng to develop and test a first-of-its-kind artificial intelligence teaching tool capable of assisting educators in grading and providing meaningful, constructive feedback for a high volume of student assignments. Their innovative tool, which is detailed in a Stanford AI Lab blogpost, exceeded their expectations. In education, it can be difficult to get lots of data for a single problem, like hundreds of instructor comments on one homework question. Companies that market online coding courses are often similarly limited, and therefore rely on multiple-choice questions or generic error messages when reviewing students' work. "This task is really hard for machine learning because you don't have a ton of data.

Computer science is about understanding computer systems and networks at a deep level. Computers and the programs they run are among the most complex products ever created; designing and using them effectively presents immense challenges. Facing these challenges is the aim of computer science as a practical discipline, and this leads to some fundamental questions. The theories that are now emerging to answer these kinds of questions can be immediately applied to design new computers, programs, networks and systems that are transforming science, business, culture and all other aspects of life. Want to find out more about Computer Science and joint degrees?

My first encounter with computer science was in grade 5, when my mom put me in my local library's C and HTML classes. At only grade 5, computer science seemed like an alien language. After struggling to write my program for hours, I gave up. I told myself that computer science was simply not for me. Fast-forward to high school, and I didn't choose any computer science courses.

There is a growing consensus that artificial intelligence ethics instruction is critical, and must extend beyond computer sciences courses. Ethics and technology have always been tightly interwoven, but as artificial intelligence (AI) marches forward and impacts society in new and novel ways, the stakes--and repercussions--are growing. "There is potential for (AI) to be used in ways that society disapproves of," observes David S. Touretzky, a research professor in the computer science department at Carnegie Mellon University. One idea that's gaining momentum is AI ethics instruction in schools. Groups such as AI4K12 and the MIT Media Lab have begun to study the issue and develop AI learning frameworks for K-12 students.

Congratulations! Data Science is a career that's hottest, hardest, most challenging, most rewarding, and full of top-notch minds. Your journey is bound to be full of fun, challenges, enlightenment, and achievements (big or small). New papers are published daily or even hourly. New techniques and experiments are developed regularly. New ways of thinking become the new norm.

Facebook and the digital virus called fake news.

Aidan Wen is well on his way toward a career in artificial intelligence. The high school junior already has two semesters of machine-learning courses under his belt. Last summer he competed for a $12,000 prize sponsored by the Radiological Society of North America for the best ML model for spotting signs of pneumonia in lung X-rays. This year, he has entered another competition seeking a system for early detection of earthquakes using audio files. Next, he wants to try his hand at a project using natural language processing.

Tyson Navarro, 10, of Fremont, Calif., learns to build code using an iPad at a youth workshop at the Apple store in 2013. Code.org said a record number of female and under-represented minority students took AP computer science classes in 2018. SAN FRANCISCO -- Female, black and Latino students took Advanced Placement computer science courses in record numbers, and rural student participation surged this year, as the College Board attracted more students to an introductory course designed to expand who has access to sought-after tech skills. This year, 135,992 students took advanced placement (AP) computer science exams, a 31 percent increase from last year, according to data from the College Board, the organization that administers standardized tests that help determine college entrances as well as AP courses. Females and under-represented minorities were among the fastest growing groups.