14 AI Summer Camps for Middle School Students
- Stephen Turban
- 2 hours ago
- 11 min read
Summer AI camps offer middle school students (grades 6–8) an affordable way to dive into advanced academics and practical, hands‑on skills. These focused, shorter programs blend project‑based learning, industry exposure, and mentorship, helping participants build connections while learning core AI concepts.
How are AI camps different from other AI programs in middle school?
Unlike longer university‑based courses, independent AI camps concentrate on a narrow set of skills over just a few days or weeks, making them more accessible and less time‑intensive for busy students. For young AI enthusiasts, a summer camp can offer early industry insights and a community of like‑minded peers.
We’ve curated this list of the top 14 AI summer camps for middle schoolers based on objective criteria such as academic rigor, networking opportunities, full or partial funding, competitive acceptance rates, and the prestige of the hosting organization.
14 AI Summer Camps for Middle School Students
Acceptance rate/cohort size: Small cohorts of 8 or fewer students.
Location: Fully online
Cost/Stipend: Starts from $2,699 for the standard 3‑week core package, with early‑bird and bundled options (e.g., with Python prep or AI extension) ranging up to $4,897. Limited scholarships may be available
Dates: July 14 – August 1
Application Deadline: Rolling admission until full
Eligibility: Ages 12–18, divided into cohorts for 12–14 and 15–18-year-olds; assumes basic Python familiarity (e.g., writing functions, using libraries)
This three-week virtual program is designed to introduce students to professional-grade data science and AI using Python. Participants build machine learning models, including linear and logistic regression, decision trees, XGBoost, and sequential neural networks while working with real‑world datasets of their choice. Daily sessions combine interactive lectures with live coding demonstrations; students then collaborate in small cohort groups (typically six per group) to apply concepts in hands-on notebooks and exercises. A final capstone project allows each student to tell a data-driven story with predictive modeling and data visualization using libraries like pandas, matplotlib, seaborn, sklearn, and Keras. Along the way, cohort leads and supportive staff maintain a staff-to-student ratio better than 4:1, offering real-time debugging and personalized feedback. At the end of the program, students receive a Data Science Scholar Certificate and access to a repository of coding notebooks, videos, and lecture guides.
Acceptance rate / Cohort size: Not published
Location: NC State University, Raleigh, NC (in-person day camp)
Cost/Stipend: $500 for a week of camp (including daily lunch). A limited amount of financial aid may be available for families who demonstrate financial need.
Dates: July 7 – 11
Application Deadline: March 28
Eligibility: Students entering grades 6–8 in the fall
This one‑week camp provides rising middle schoolers with a hands‑on introduction to coding, computational thinking, and scientific inquiry. Through structured workshops, students engage in design‑based coding activities, such as sketch‑based science modeling tools, to help them understand data and science concepts using code. They work collaboratively in small teams, using block‑ or Python‑based environments to create visual models and simple simulations. The camp includes mentorship from NC State computer science undergraduates and faculty, fostering real‑time feedback and peer learning. On the final day, teams showcase their science‑coded models or games in a group presentation. Overall, the program emphasizes exploration of science and computing, integrating AI‑powered storytelling and modeling techniques.
Acceptance rate/cohort size: Highly competitive. Lumiere receives over 5,000 applications annually.
Location: Online
Dates: Varies by cohort.
Application deadline: Varies by cohort.
Eligibility: Students in grades 6-8
The Lumiere Junior Explorer Program is a virtual program for middle school students who want to develop an independent research project under the guidance of a mentor. You get to choose from a number of available research areas, one of which is AI & Data Science. In this track, you will learn the fundamentals of the field, such as natural language processing, data analysis and visualization, and face/voice recognition. You will then dive into a specific topic and develop your independent project through weekly, 1-on-1 sessions with your mentor. Aside from having a completed research paper/presentation at the end of the program, you will also improve your writing, critical thinking, and problem-solving skills.
Acceptance rate/cohort size: Exact acceptance rate is not published, but the program maintains a small group format with a 5:1 student-to-mentor ratio, suggesting selective cohort sizes
Location: Fully online
Dates: Varies by cohort.
Application Deadline: Varies by cohort.
Eligibility: Open to middle school students in grades 6–8; no prior experience required
The AI Trailblazers program is a 25-hour, cohort-based virtual experience where students develop core AI and Python skills alongside peers. Through structured weekend sessions or packed weekday modules, students explore topics including regression, image classification, neural networks, deep learning basics, sentiment analysis, and AI ethics. Learning combines theory lectures with group sessions, capped at a 5:1 student‑to‑mentor ratio to ensure focused feedback and personalized guidance. Small teams of three to five students collaborate on a culminating group project with past examples including building algorithms to classify music genres or detect propaganda in speeches. Instructors guide students through the full project lifecycle, including data collection, model building, and presentation, offering opportunities to develop critical thinking, technical communication, and teamwork in a real-world context. With emphasis on collaboration, mentorship, and applied learning, this program delivers a rigorous yet accessible introduction to AI fundamentals for middle schoolers.
Acceptance rate / Cohort size: Not publicly disclosed
Location: NC State University, Raleigh, NC (in-person day camp)
Cost/Stipend: $500 for a week of camp (including daily lunch). A limited amount of financial aid may be available for families who demonstrate financial need.
Dates: July 21 – 25
Application Deadline: March 28
Eligibility: Students entering grades 6–8 in the fall
This week‑long AI exploration camp introduces middle schoolers to core concepts such as perception, machine learning, natural interaction, and AI ethics through game‑oriented challenges. Participants work in small teams to build interactive game elements powered by AI. For instance, designing conversational non‑player characters or programming a robotic ball to navigate an obstacle course using sensor logic. Using game design tasks, the camp blends coding fundamentals with creative problem-solving. NC State faculty and undergraduate mentors guide students through debugging, design iteration, and ethical considerations. On the final day, teams present their AI game prototypes to peers and instructors. With a focus on collaboration, creativity, and applied learning, this program offers a game-oriented introduction to AI for middle schoolers.
Acceptance rate / Cohort size: Not published; cohort sizes are modest to allow hands‑on supervision (typical classroom ratio 1:6)
Cost/Stipend: $500 for the week; need‑based financial aid is available
Dates: July 7–11 | July 14-18
Application Deadline: March 28
Eligibility: Open to rising 6th–8th graders in fall
This one‑week day camp introduces rising middle school students to foundational engineering and coding through project-based learning. Students work in small teams to explore science and computational thinking via design challenges such as modeling structures or scientific simulations using block‑based or Python coding in a collaborative setting. Mentorship from NC State engineering faculty, K–12 teachers, and undergraduate students provides real-time feedback and encouragement. Participants develop skills around the engineering design process: ask, imagine, plan, create, and improve their coded models. Camp culminates in a group presentation where each team demonstrates a science-coded model or game that reflects both technical understanding and creativity. With its combination of hands-on projects and collaborative learning, this camp blends science, coding, and AI storytelling in an age‑appropriate format.
Acceptance rate / Cohort size: Not applicable
Location: Fully online (self‑paced)
Cost/Stipend: Completely free
Dates: This is a self-paced program that takes place over 10 hours
Application Deadline: None
Eligibility: All upper elementary and middle school students
The Creativity & AI Workshop from MIT RAISE offers a free, curriculum‑based introduction to AI-powered creative tools designed for upper elementary and middle school students. Participants and their parent or teacher mentors work through hands-on modules involving neural networks and generative adversarial networks (GANs) to explore AI-generated art in text, images, music, and video. The curriculum emphasizes creative expression alongside ethical reflection. Students engage in guided discussions around ownership, deepfakes, and the role of AI in media and art creation. Lessons are structured to be inquiry-based and accessible: each activity is modular (30–60 minutes) and includes educator guides for safe implementation at home or in a classroom. Though not cohort-based, the program encourages collaborative exploration through optional peer showcase or classroom sharing. With its emphasis on integrating technical literacy and ethics, this workshop empowers young learners to imagine and critique generative AI, preparing them for creative and responsible engagement with new technologies. You can go through the full curriculum here.
Acceptance rate / Cohort size: Highly competitive. Although precise acceptance rates are not public, past cohorts numbered about 65 participants across grades 8–10
Location: On‑campus commuter day program at NYU Tandon School of Engineering in Brooklyn, NY
Cost/Stipend: Completely free for accepted participants; tuition and program costs covered by sponsors such as National Grid. Housing and meals are not included.
Dates: July 7 – August 1
Application Deadline: May 15
Eligibility: Applicants must reside in New York City, be between the ages of 12 and 14 by program start (typically entering 7th or 8th grade), and demonstrate strong STEM interest
NYU’s Science of Smart Cities immerses middle schoolers in sustainable urban engineering, IoT, and foundational coding through a four‑week, in-person program. Students work in teams to design smart-city prototypes such as solar-powered sensors, smart bridges, climate-measuring drones, and IoT-connected infrastructure, drawing on electronics, microcontrollers, and circuitry. Coursework spans urban systems like energy, transportation, environmental monitoring, and wireless communication. Mentored by NYU Tandon faculty, graduate students, and theater professionals from Irondale, participants also receive training in public speaking and pitching. The program culminates in a public SoSC Expo, where teams present their prototypes to engineers, planners, and the community. Though the curriculum doesn’t center on AI exclusively, projects often involve sensor-driven data collection and basic machine learning concepts, making this fully funded, cohort-based program a prestigious and hands-on introduction to STEM and smart city innovation.
Acceptance rate / Cohort size: Not applicable
Location: Fully online, self-paced course
Cost/Stipend: Free for all students
Dates: Self-paced and available at any time; total engagement roughly 1 hour, so it can be completed flexibly throughout the summer
Application Deadline: None
Eligibility: Open to students in grades 3–12
AI for Oceans is a one-hour interactive tutorial where middle schoolers explore how machine learning works by training a model to distinguish between fish and trash in the ocean. Along the way, learners test the AI’s performance using labeled examples and experiment with more subjective categories like “triangular” or “angry” fish to reveal how human bias impacts training and predictions. The activity integrates short explanatory videos covering topics like training data, bias, and societal impact, fostering early awareness of ethics in AI. While not a cohort-based camp, the tutorial encourages students to play the role of both teacher and model developer, fast-tracking their understanding of AI fundamentals in a hands-on way. This program provides an accessible entry into AI without any cost, time commitment, or need for prior coding experience.
Acceptance rate / Cohort size: Not applicable
Location: Virtual
Cost: Free
Application Deadline: None; only registration is required at the time of starting a course.
Program Dates: The courses are available all year round.
Eligibility: Open to everyone
A self-paced learning program offered by Google AI, these courses focus on generative AI and machine learning. They help you build a solid foundation for future research or study in the field of computer science and AI. As a middle school student, these courses are pretty well-paced and cover complex topics in AI in a simple manner. Some of the topics covered include linear algebra for AI and ML, machine learning problem framing, and the basics of machine learning.
These courses are purely self-paced, so it is up to you to set aside learning time during your summer break or even during your school year!
Acceptance rate / Cohort size: Not publishedLocation: University of Texas at San Antonio (UTSA), San Antonio, TX
Cost/Stipend: $200; reduced tuition available for students qualifying for free or reduced-price lunch programs
Dates (Summer): June 9 – 12
Application Deadline: Applications usually open at xyz year on year. Check the website for updates.
Eligibility: Open to students entering grades 6–8 in Fall
UTSA’s “Artificial Intelligence for Everyone!” is a four-day, in-person summer camp for middle school students eager to explore the fundamentals of AI and machine learning. Through a combination of classroom instruction and hands-on learning, campers engage in image processing, generative AI, and machine learning projects, even creating their own AI app to address a practical problem. The curriculum introduces concepts like training data, model evaluation, and responsible AI practices in a way that requires no prior coding experience. UTSA faculty and graduate assistants guide each student through practical labs, demonstrations, and peer collaboration. The camp is a project-based learning experience that emphasizes active experimentation, critical thinking, and applied AI literacy.
Acceptance rate / Cohort size: Exact cohort size not disclosed.
Location: Fully online (live Zoom sessions)
Cost/Stipend: Completely free
Dates: July 8-19 (tentative)
Application Deadline: Applications usually open at xyz year on year. Check the website for updates.
Eligibility: Open to middle school and high school students
Community AI’s AI Summer Camp enables middle school students to engage in a live, mentor-led AI learning journey at no cost. Instructors, including a Fortune 500 lead data scientist and machine learning-certified student mentors from institutions like Stanford and IBM, deliver interactive lessons over Zoom. You will work on hands-on assignments and develop a final project focused on applying AI for environmental or community impact. The curriculum covers machine learning basics, coding constructs, ethical considerations, and social applications of AI. Students work collaboratively in guided cohorts and receive personalized feedback. Completing at least 80% of the program and the final project earns participants a certificate from Community AI Inc., and standout projects may be featured on their website.
Acceptance rate/cohort size: Small-Groups
Location: Various U.S. locations, including prestigious universities like Carnegie Mellon University (PA) and Stanford University (CA)
Cost/Stipend: Starting at $1,199
Dates: The program runs for a week. Exact dates vary by location, so be sure to check the website for the latest details.
Application Deadline: Deadlines vary depending on location. Check the website for specific dates.
Eligibility: Ages 13-17
AIClub’s Summer Camp introduces young learners to classification, regression, machine learning models, and real-world AI applications in fields like weather prediction or image recognition. Taught by PhD-level instructors and industry professionals, students engage in five interactive, project-oriented live sessions lasting roughly three hours each. You collaborate on practical AI challenges such as building predictive models for datasets while learning to apply AI concepts in a hands-on context. The curriculum is beginner-friendly, integrating ethical considerations like bias and fairness alongside technical lessons. Teams work together during camp to develop mini AI projects, receive live mentorship, and present their results at the end. AIClub offers an introductory AI program for middle school students without overwhelming technical demands.
Location: Virtual
Cost: Not specified
Program Dates: The camp consists of 5 sessions, each lasting 3 hours. Specific dates are yet to be announced
Application Deadline: Refer to the website for admission dates and deadlines
Eligibility: Students in Grades 6-10 with no prior experience in AI or programming required
This live, five-day online camp introduces middle school students to core AI principles such as classification, regression, and model training, all through hands-on Python tutorials and project work. Each day includes interactive lessons led by PhD-level mentors, progressing from basic AI concepts to building personalized AI applications like chatbots and image recognizers. You will construct and refine project-based AI applications (for instance, weather predictors or image classifiers), debug code in real time, and present your final creations in a showcase format on the last day of camp. Ethical considerations around AI, such as fairness, data bias, and responsible design, are woven into lessons and discussions. Upon completion, participants receive a certificate and access to AIClub’s cloud-based project platform to continue building independently.
Stephen is one of the founders of Lumiere and a Harvard College graduate. He founded Lumiere as a Ph.D. student at Harvard Business School. Lumiere is a selective research program in which students work one-on-one with a mentor to develop an independent research paper.
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