Artificial Intelligence (AI) and cybersecurity are becoming increasingly intertwined, with AI and Machine Learning (AI/ML) being leveraged for cybersecurity, and cybersecurity helping address issues caused by AI. The goal in our exploratory curricular initiative is to dovetail the need to teach these two critical, emerging topics in highschool, and create a suite of novel activities, 'AI & Cybersecurity for Teens' (ACT) that introduces AI/ML in the context of cybersecurity and prepares high school teachers to integrate them in their cybersecurity curricula. Additionally, ACT activities are designed such that teachers (and students) build a deeper understanding of how ML works and how the machine actually "learns". Such understanding will aid more meaningful interrogation of critical issues such as AI ethics and bias. ACT introduces core ML topics contextualized in cybersecurity topics through a range of programming activities and pre-programmed games in NetsBlox, an easy-to-use block-based programming environment. We conducted 2 pilot workshops with 12 high school cybersecurity teachers focused on ACT activities. Teachers' feedback was positive and encouraging but also highlighted potential challenges in implementing ACT in the classroom. This paper reports on our approach and activities design, and teachers' experiences and feedback on integrating AI into high school cybersecurity curricula.
more »
« less
This content will become publicly available on June 25, 2024
Conducting the Pilot Study of Integrating AI: An Experience Integrating Machine Learning into Upper Elementary Robotics Learning (Work in Progress)
Artificial Intelligence (AI) enhanced systems are widely adopted in post-secondary education, however, tools and activities have only recently become accessible for teaching AI and machine learning (ML) concepts to K-12 students. Research on K-12 AI education has largely included student attitudes toward AI careers, AI ethics, and student use of various existing AI agents such as voice assistants; most of which has focused on high school and middle school. There is no consensus on which AI and Machine Learning concepts are grade-appropriate for elementary-aged students or how elementary students explore and make sense of AI and ML tools. AI is a rapidly evolving technology and as future decision-makers, children will need to be AI literate[1]. In this paper, we will present elementary students’ sense-making of simple machine-learning concepts. Through this project, we hope to generate a new model for introducing AI concepts to elementary students into school curricula and provide tangible, trainable representations of ML for students to explore in the physical world. In our first year, our focus has been on simpler machine learning algorithms. Our desire is to empower students to not only use AI tools but also to understand how they operate. We believe that appropriate activities can help late elementary-aged students develop foundational AI knowledge namely (1) how a robot senses the world, and (2) how a robot represents data for making decisions. Educational robotics programs have been repeatedly shown to result in positive learning impacts and increased interest[2]. In this pilot study, we leveraged the LEGO® Education SPIKE™ Prime for introducing ML concepts to upper elementary students. Through pilot testing in three one-week summer programs, we iteratively developed a limited display interface for supervised learning using the nearest neighbor algorithm. We collected videos to perform a qualitative evaluation. Based on analyzing student behavior and the process of students trained in robotics, we found some students show interest in exploring pre-trained ML models and training new models while building personally relevant robotic creations and developing solutions to engineering tasks. While students were interested in using the ML tools for complex tasks, they seemed to prefer to use block programming or manual motor controls where they felt it was practical.
more »
« less
- Award ID(s):
- 2119174
- NSF-PAR ID:
- 10463364
- Date Published:
- Journal Name:
- ASEE annual conference exposition proceedings
- ISSN:
- 2153-5868
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
null (Ed.)In recent years, it has increasingly been recognized that spatial visualization skills are important in supporting student success in Science, Technology, Engineering, and Math (STEM) education and retention of these students in STEM careers. Many first year college engineering programs and high schools with pre-engineering curriculum have incorporated spatial visualization training into their courses. However, there is no reason why spatial visualization training could not occur at a much younger age, like elementary school. Often at the high school and college level, the Purdue Spatial Visualization Test: Rotations (PSVT:R), which is recognized as a gold standard assessment tool, is used to measure students’ spatial skills learning gains. The PSVT:R is a 20 minute timed test consisting of 30 three-dimensional rotations problems. While the PSVT:R test has been well validated, there are benefits to developing alternative methods of assessing spatial visualization skills suitable for elementary school grades. Researchers at XXX developed an assembly pre- and post- test based upon a timed Lego™ exercise. Students are timed to see how long it would take them to build small Lego shapes using only a picture of the final assembly, but no instructions. The test was implemented at the beginning and then at the end of the quarter/semester. The beauty of this assessment is that it lends itself well to K-12 students. The 20 minute, timed PSVT:R test is too challenging for elementary aged children and is not engaging. In order to validate the new instrument, the Lego™ Assembly test was implemented in a pilot study in 2 college freshman engineering courses using students who could do both the PSVT:R and the Lego Assembly™ assessments. At the beginning of the class all students took the PSVT:R test, and half the students performed a Lego™ assembly of one shape and the other half did the assembly test with another shape. During the class the students completed spatial visualization training which taught them how to sketch orthographic and isometric assignments using a mobile sketching app. At the end of the class the PSVT:R test was repeated for all students. The Lego Assembly™ test was also completed, but the students switched which shape they were building. This approach allowed us to normalize the difficulty of the assembly tasks based upon the average time it took to build the shapes in the pre-test. The Lego Assembly™ test was first implemented in winter and spring of 2018. The data showed a correlation of the R-Squared of 0.11 between the assembly times and the PSVT:R scores in pre-test and 0.14 in post-test. However, analysis of the assembly times indicated that the difficulty of the 2 Lego shapes were significantly different, which could skew the normalization of the assembly times. Accordingly, the test was repeated in winter and spring 2019 with Lego shapes of similar difficulty. This paper describes the results of the assembly tess in all 4 classes, and its suitability for a spatial visualization assessment for elementary school age students.more » « less
-
Existing approaches to teaching artifcial intelligence and machine learning (ML) often focus on the use of pre-trained models or fne-tuning an existing black-box architecture. We believe ML techniques and core ML topics, such as optimization and adversarial examples, can be designed for high school age students given appropriate support. Our curricular approach focuses on teaching ML ideas by enabling students to develop deep intuition about these complex concepts by first making them accessible to novices through interactive tools, pre-programmed games, and carefully designed programming activities. Then, students are able to engage with the concepts via meaningful, hands-on experiences that span the entire ML process from data collection to model optimization and inspection. This paper describes our AI & Cybersecurity for Teens suite of curricular activities aimed at high school students and teachers.more » « less
-
Major challenges in engineering education include retention of undergraduate engineering students (UESs) and continued engagement after the first year when concepts increase in difficulty. Additionally, employers, as well as ABET, look for students to demonstrate non-technical skills, including the ability to work successfully in groups, the ability to communicate both within and outside their discipline, and the ability to find information that will help them solve problems and contribute to lifelong learning. Teacher education is also facing challenges given the recent incorporation of engineering practices and core ideas into the Next Generation Science Standards (NGSS) and state level standards of learning. To help teachers meet these standards in their classrooms, education courses for preservice teachers (PSTs) must provide resources and opportunities to increase science and engineering knowledge, and the associated pedagogies. To address these challenges, Ed+gineering, an NSF-funded multidisciplinary collaborative service learning project, was implemented into two sets of paired-classes in engineering and education: a 100 level mechanical engineering class (n = 42) and a foundations class in education (n = 17), and a fluid mechanics class in mechanical engineering technology (n = 23) and a science methods class (n = 15). The paired classes collaborated in multidisciplinary teams of 5-8 undergraduate students to plan and teach engineering lessons to local elementary school students. Teams completed a series of previously tested, scaffolded activities to guide their collaboration. Designing and delivering lessons engaged university students in collaborative processes that promoted social learning, including researching and planning, peer mentoring, teaching and receiving feedback, and reflecting and revising their engineering lesson. The research questions examined in this pilot, mixed-methods research study include: (1) How did PSTs’ Ed+gineering experiences influence their engineering and science knowledge?; (2) How did PSTs’ and UESs’ Ed+gineering experiences influence their pedagogical understanding?; and (3) What were PSTs’ and UESs’ overall perceptions of their Ed+gineering experiences? Both quantitative (e.g., Engineering Design Process assessment, Science Content Knowledge assessment) and qualitative (student reflections) data were used to assess knowledge gains and project perceptions following the semester-long intervention. Findings suggest that the PSTs were more aware and comfortable with the engineering field following lesson development and delivery, and often better able to explain particular science/engineering concepts. Both PSTs and UESs, but especially the latter, came to realize the importance of planning and preparing lessons to be taught to an audience. UESs reported greater appreciation for the work of educators. PSTs and UESs expressed how they learned to work in groups with multidisciplinary members—this is a valuable lesson for their respective professional careers. Yearly, the Ed+gineering research team will also request and review student retention reports in their respective programs to assess project impact.more » « less
-
Engineering Explorations are curriculum modules that engage children across contexts in learning about science and engineering. We used them to leverage multiple education sectors (K–12 schools, museums, higher education, and afterschool programs) across a community to provide engineering learning experiences for youth, while increasing local teachers’ capacity to deliver high-quality engineering learning opportunities that align with school standards. Focusing on multiple partners that serve youth in the same community provides opportunities for long-term collaborations and programs developed in response to local needs. In a significant shift from earlier sets of standards, the Next Generation Science Standards include engineering design, with the goal of providing students with a foundation “to better engage in and aspire to solve the major societal and environmental challenges they will face in decades ahead” (NGSS Lead States 2013, Appendix I). Including engineering in K–12 standards is a positive step forward in introducing students to engineering; however, K–12 teachers are not prepared to facilitate high-quality engineering activities. Research has consistently shown that elementary teachers are not confident in teaching science, especially physical science, and generally have little knowledge of engineering (Trygstad 2013). K–12 teachers, therefore, will need support. Our goal was to create a program that took advantage of the varied resources across a STEM (science, technology, engineering, and math) education ecosystem to support engineering instruction for youth across multiple contexts, while building the capacity of educators and meeting the needs of each organization. Specifically, we developed mutually reinforcing classroom and field trip activities to improve student learning and a curriculum to improve teacher learning. This challenging task required expertise in school-based standards, engineering education, informal education, teacher professional development, and classroom and museum contexts.more » « less
