With ChatGPT, do we have to rewrite our learning objectives -- CASE study in Cybersecurity

• URL: http://arxiv.org/abs/2311.06261v1
• Date: Tue, 26 Sep 2023 16:12:10 GMT
• Title: With ChatGPT, do we have to rewrite our learning objectives -- CASE study in Cybersecurity
• Authors: Peter Jamieson, Suman Bhunia, Dhananjai M. Rao
• Abstract summary: We make a case study of cybersecurity undergrad education by using the lens of Understanding by Design'' (UbD) With these details, we perform a thought experiment on how attainable the learning objectives (LOs) are with the above-described tools. Our goal is to push all of us to leverage and teach these tools as powerful allies in our quest to improve human existence and knowledge.
• Score: 0.9208007322096533
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: With the emergence of Artificial Intelligent chatbot tools such as ChatGPT and code writing AI tools such as GitHub Copilot, educators need to question what and how we should teach our courses and curricula in the future. In reality, automated tools may result in certain academic fields being deeply reduced in the number of employable people. In this work, we make a case study of cybersecurity undergrad education by using the lens of ``Understanding by Design'' (UbD). First, we provide a broad understanding of learning objectives (LOs) in cybersecurity from a computer science perspective. Next, we dig a little deeper into a curriculum with an undergraduate emphasis on cybersecurity and examine the major courses and their LOs for our cybersecurity program at Miami University. With these details, we perform a thought experiment on how attainable the LOs are with the above-described tools, asking the key question ``what needs to be enduring concepts?'' learned in this process. If an LO becomes something that the existence of automation tools might be able to do, we then ask ``what level is attainable for the LO that is not a simple query to the tools?''. With this exercise, we hope to establish an example of how to prompt ChatGPT to accelerate students in their achievements of LOs given the existence of these new AI tools, and our goal is to push all of us to leverage and teach these tools as powerful allies in our quest to improve human existence and knowledge.

Related papers

• ChatGPT in Classrooms: Transforming Challenges into Opportunities in Education [0.14732811715354452]
  We are standing on the brink of this educational frontier, and it is clear that we need to navigate this terrain with a lot of care. When used inappropriately, AI tools can become the perfect tool for the cut copy paste mentality. This study will be used as a process manual for future researchers to apply, running their own data.
  arXiv  Detail & Related papers  (2024-05-17T09:17:59Z)
• Beyond Traditional Teaching: The Potential of Large Language Models and Chatbots in Graduate Engineering Education [0.0]
  This paper explores the potential integration of large language models (LLMs) and chatbots into graduate engineering education. We develop a question bank from the course material and assess the bot's ability to provide accurate, insightful responses. We demonstrate how powerful plugins like Wolfram Alpha for mathematical problem-solving and code interpretation can significantly extend the bot's capabilities.
  arXiv  Detail & Related papers  (2023-09-09T13:37:22Z)
• Brain-Inspired Computational Intelligence via Predictive Coding [89.6335791546526]
  Predictive coding (PC) has shown promising performance in machine intelligence tasks. PC can model information processing in different brain areas, can be used in cognitive control and robotics.
  arXiv  Detail & Related papers  (2023-08-15T16:37:16Z)
• Tool Learning with Foundation Models [114.2581831746077]
  With the advent of foundation models, AI systems have the potential to be equally adept in tool use as humans. Despite its immense potential, there is still a lack of a comprehensive understanding of key challenges, opportunities, and future endeavors in this field.
  arXiv  Detail & Related papers  (2023-04-17T15:16:10Z)
• On the Educational Impact of ChatGPT: Is Artificial Intelligence Ready to Obtain a University Degree? [0.0]
  We evaluate the influence of ChatGPT on university education. We discuss how computer science higher education should adapt to tools like ChatGPT.
  arXiv  Detail & Related papers  (2023-03-20T14:27:37Z)
• Smart tutor to provide feedback in programming courses [0.0]
  We present an AI based intelligent tutor that answers students programming questions. The tool has been tested by university students at the URJC along a whole course.
  arXiv  Detail & Related papers  (2023-01-24T11:00:06Z)
• Tiny Robot Learning: Challenges and Directions for Machine Learning in Resource-Constrained Robots [57.27442333662654]
  Machine learning (ML) has become a pervasive tool across computing systems. Tiny robot learning is the deployment of ML on resource-constrained low-cost autonomous robots. Tiny robot learning is subject to challenges from size, weight, area, and power (SWAP) constraints. This paper gives a brief survey of the tiny robot learning space, elaborates on key challenges, and proposes promising opportunities for future work in ML system design.
  arXiv  Detail & Related papers  (2022-05-11T19:36:15Z)
• A Classification of Artificial Intelligence Systems for Mathematics Education [3.718476964451589]
  This chapter provides an overview of the different Artificial Intelligence (AI) systems that are being used in digital tools for Mathematics Education (ME) It is aimed at researchers in AI and Machine Learning (ML), for whom we shed some light on the specific technologies that are being used in educational applications.
  arXiv  Detail & Related papers  (2021-07-13T12:09:10Z)
• Actionable Models: Unsupervised Offline Reinforcement Learning of Robotic Skills [93.12417203541948]
  We propose the objective of learning a functional understanding of the environment by learning to reach any goal state in a given dataset. We find that our method can operate on high-dimensional camera images and learn a variety of skills on real robots that generalize to previously unseen scenes and objects.
  arXiv  Detail & Related papers  (2021-04-15T20:10:11Z)
• Automatic Curriculum Learning through Value Disagreement [95.19299356298876]
  Continually solving new, unsolved tasks is the key to learning diverse behaviors. In the multi-task domain, where an agent needs to reach multiple goals, the choice of training goals can largely affect sample efficiency. We propose setting up an automatic curriculum for goals that the agent needs to solve. We evaluate our method across 13 multi-goal robotic tasks and 5 navigation tasks, and demonstrate performance gains over current state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-06-17T03:58:25Z)
• Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense [142.53911271465344]
  We argue that the next generation of AI must embrace "dark" humanlike common sense for solving novel tasks. We identify functionality, physics, intent, causality, and utility (FPICU) as the five core domains of cognitive AI with humanlike common sense.
  arXiv  Detail & Related papers  (2020-04-20T04:07:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.