Biological HCI (Bio-HCI) framework is a design framework that investigate the relationship between human, computer and biological systems by redefining biological materials as design elements. Bio-HCI focuses on three major components: biological materials, intermediate platforms, and interactions with the user. This framework is created through collaboration between biotechnologists, HCI researchers, and speculative design researchers. To examine this framework further, we present four experiments which focus on different aspects of the Bio-HCI framework. The goal of this paper is to 1) layout the framework for Bio-HCI 2) explore the applications of biological - digital interfaces 3) analyze existing technologies and identify opportunities for future research.

Biological HCI: Towards Integrative Interfaces Between People, Computer, and Biological Materials

Over the past decades, numerous practical applications of machine learning techniques have shown the potential of AI-driven and data-driven approaches in a large number of computing fields. Machine learning is increasingly included in computing curricula in higher education, and a quickly growing number of initiatives are expanding it in K–12 computing education, too. As machine learning enters K–12 computing education, understanding how intuition and agency in the context of such systems is developed becomes a key research area. But as schools and teachers are already struggling with integrating traditional computational thinking and traditional artificial intelligence into school curricula, understanding the challenges behind teaching machine learning in K–12 is an even more daunting challenge for computing education research. Despite the central position of machine learning and AI in the field of modern computing, the computing education research body of literature contains remarkably few studies of how people learn to train, test, improve, and deploy machine learning systems. This is especially true of the K–12 curriculum space. This article charts the emerging trajectories in educational practice, theory, and technology related to teaching machine learning in K–12 education. The article situates the existing work in the context of computing education in general, and describes some differences that K–12 computing educators should take into account when facing this challenge. The article focuses on key aspects of the paradigm shift that will be required in order to successfully integrate machine learning into the broader K–12 computing curricula. A crucial step is abandoning the belief that rule-based “traditional” programming is a central aspect and building block in developing next generation computational thinking.

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Teaching Machine Learning in K–12 Classroom: Pedagogical and Technological Trajectories for Artificial Intelligence Education

Artificial intelligence (AI) education has posed fundamental challenges to early childhood education (ECE), including (1) why AI is necessary and appropriate for learning in the early years, (2) what is the subset of key AI ideas and concepts that can be learned by children, and (3) how to engage children in a meaningful experience that allows them to acquire these fundamental AI concepts. This report from the ECE field discusses the key considerations for developing an AI curriculum for young children. These key considerations altogether present an innovative pedagogical model for AI literacy education in early childhood. This model argues that AI literacy is an organic part of digital literacy for all citizens in an increasingly intelligent society. The core AI knowledge that can be explored with young children is: Using large amounts of data input, AI algorithms can be continuously trained to identify patterns, make predictions, and recommend actions, even though with limitations. Based on the theoretical notions of learning-by-making and pedagogy-as-relational, an embodied, culturally responsive approach should be used to enable young children's exploration with AI technologies. Finally, an exemplary curriculum named “AI for Kids” is introduced to demonstrate this pedagogical model and explain how educators can provide children culturally responsive inquiry opportunities to interact with and understand AI technologies. The synthesis of knowledge regarding “Why”, “What”, and “How” to do with AI education for young children informs a new way to engage children in STEM and understanding the digital world. 

Artificial Intelligence education for young children: Why, what, and how in curriculum design and implementation

Artificial Intelligence (AI) education is an increasingly popular topic area for K-12 teachers. However, little research has investigated how AI education can be designed to be more accessible to all learners. We organized co-design workshops with 15 K-12 teachers to identify opportunities to integrate AI education into core curriculum to leverage learners' interests. During the co-design workshops, teachers and researchers co-created lesson plans where AI concepts were embedded into various core subjects. We found that K-12 teachers need additional scaffolding in the curriculum to facilitate ethics and data discussions, and value supports for learner engagement, collaboration, and reflection. We identify opportunities for researchers and teachers to collaborate to make AI education more accessible, and present an exemplar lesson plan that shows entry points for teaching AI in non-computing subjects. We also reflect on co-designing with K-12 teachers in a remote setting.

Engaging Teachers to Co-Design Integrated AI Curriculum for K-12 Classrooms.

Artificial intelligence (AI) literacy is a rapidly growing research area and a critical addition to K-12 education. However, support for designing tools and curriculum to teach K-12 AI literacy is still limited. There is a need for additional interdisciplinary human-computer interaction and education research investigating (1) how general AI literacy is currently implemented in learning experiences and (2) what additional guidelines are required to teach AI literacy in specifically K-12 learning contexts. In this paper, we analyze a collection of K-12 AI and education literature to show how core competencies of AI literacy are applied successfully and organize them into an educator-friendly chart to enable educators to efficiently find appropriate resources for their classrooms. We also identify future opportunities and K-12 specific design guidelines, which we synthesized into a conceptual framework to support researchers, designers, and educators in creating K-12 AI learning experiences.

Designing AI Learning Experiences for K-12: Emerging Works, Future Opportunities and a Design Framework.

We present an approach in STEM education at the intersection of machine learning, gamification, and social context based in Thailand. We designed an agricultural based AI challenge that fostered students to learn the process of creating machine learning models in the form of a game with the emphasis on the Four P's of Creative Learning (Projects, Passion, Play, and Peers). Our goal is to come up with an innovative education model that encourages the students to connect the emerging technological solutions such as AI with the pressing realworld problems in the playful environment. We found that machine learning can be used as a tool to successfully conduct interdisciplinary education at the middle school level.

Kids making AI: Integrating Machine Learning, Gamification, and Social Context in STEM Education

Problems like robotic navigation and automatic geocoding of businesses require artificial agents to perform rapid and accurate word recognition in natural images. We set out to develop a deep learning method to recognize words from different languages in captured images, with high accuracy and with small number of captured samples. Our experiments reveal three main findings. First, we feed images of words as inputs to the neural network directly, omitting segmentation and postprocessing step to avoid compound errors. We found this method to work well for our samples. Second, we are able to train machine learning models to recognize words using purely synthetic training samples by applying feature extractions to both training and testing datasets prior to passing them through deep networks. This achievement allows us to train neural network cheaply on synthetic data and transfer knowledge to recognize words in real data. Third, we set up experiments to compare model performances when using Canny edge detection and Chu’s 3D thinning algorithm as preprocessing methods. We found that Canny edge detection performs better in most cases.

Word Recognition in Captured Images by CNN Trained with Synthetic Images

Based on the context of higher life expectancy and lower pregnancy rate, ageing society is unavoidable. Technological response is necessary. Hormone Couture exhibition presents two wearable technologies that aesthetically respond to women's reproductive hormone and body temperature changes. The designs address how technology creates a biopolitical issues whether the wearable technology empowers women by giving her access to biological information, or creates gendered oppression by having a technology that monitors, controls, and influences sexual behavior on the body. Both of these arguments cannot be separated as they altogether exist at the same time as two sides of the same coin. This contradictory relationship between technological advancement and gender politics was materialized into the work aiming to criticize and speculate the future of wearable technology, and how it reinforces dynamics of gender tension in society.

Hormone couture: biopolitics, aesthetics, and technology

This paper focuses on the design and implementation of an anthropomorphic robotic finger, that was designed for teleoperation systems. This open-source anthropomorphic finger is easy to fabricate with a 3D printer and standard parts. The finger has three joints and two active Degrees of Freedom (dofs) with 2 servo motors dedicated to finger motion. Size and weight have been optimized in order to achieve human-like movement and space for attached feedback sensors. This proposed the design process, evaluate the finger with quantitative measures, both equation of four-bar linkage mechanism and equation of kinematic.

Bawornsak Sakulkueakulsuk

Papers

Kids making AI: Integrating Machine Learning, Gamification, and Social Context in STEM Education

Word Recognition in Captured Images by CNN Trained with Synthetic Images

Hormone couture: biopolitics, aesthetics, and technology

Design of an Open Source Anthropomorphic Robotic Finger for Telepresence Robot