[et_pb_section fb_built=”1″ _builder_version=”4.24.0″ _module_preset=”default” background_color=”RGBA(255,255,255,0)” global_colors_info=”{}”][et_pb_row _builder_version=”4.24.0″ _module_preset=”default” custom_padding=”||2px|||” locked=”off” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.19.2″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.24.0″ _module_preset=”c417ef0d-e927-4f7a-92d4-2634b80dd8a6″ link_font=”|800||on|||||” link_text_color=”#6FCAFC” link_font_size=”13px” link_letter_spacing=”1px” header_text_color=”#194588″ custom_margin=”||10px||false|false” hover_enabled=”0″ locked=”off” global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n
Home<\/a> Research<\/a>Poster<\/a><\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.24.0″ _module_preset=”_initial” header_font=”|700|||||||” header_text_color=”#FFFFFF” header_font_size=”50px” header_line_height=”1.2em” text_orientation=”center” custom_margin=”||10px||false|false” header_font_size_tablet=”40px” header_font_size_phone=”24px” header_font_size_last_edited=”on|desktop” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.24.0″ _module_preset=”default” text_font=”||||||||” text_text_color=”#ffffff” global_colors_info=”{}”]<\/p>\n Project Members: 1<\/sup>Andrea Ramirez-Salgado, 2<\/sup>Tanvir Hossain, 2<\/sup>Tamzidul Hoque, PhD., 1<\/sup>Swarup Bhunia, PhD., 1<\/sup>Mary Jo Koroly, PhD., Bradford Davey, 1<\/sup>Pavlo \u201cPasha\u201d Antonenko, PhD. 1<\/sup>University of Florida, 2<\/sup>University of Kansas<\/em><\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ make_equal=”on” _builder_version=”4.24.0″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.24.0″ _module_preset=”default” background_color=”rgba(34,85,85,0.61)” custom_padding=”32px|36px|32px|36px|true|true” global_colors_info=”{}”][et_pb_text _builder_version=”4.24.0″ _module_preset=”default” header_2_font=”|700|||||||” header_2_font_size=”24px” custom_margin=”||1px|||” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.24.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n A matter of national security for the US is supporting the development of the semiconductor and microchip workforce to address the chip shortage and its negative consequences.<\/p>\n<\/li>\n Higher education institutions are establishing programs to train the necessary workforce and address the CHIPS and Science Act passed by US legislature in 2022.<\/p>\n<\/li>\n Students pursuing engineering degrees are more inclined to We have undertaken a project funded by the NSF IUSE program to explore the usability and feasibility of AI-focused hardware activities specifically for first-year undergraduate students across various engineering disciplines.<\/p>\n<\/li>\n We aim to demystify AI and expand conceptions of ML to cultivate situational interest in computer hardware design and support hardware engineering career choice.<\/p>\n<\/li>\n<\/ul>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.24.0″ _module_preset=”default” background_color=”rgba(34,85,85,0.61)” custom_padding=”32px|36px|32px|36px|true|true” global_colors_info=”{}”][et_pb_text _builder_version=”4.24.0″ _module_preset=”default” header_2_font=”|700|||||||” header_2_font_size=”24px” custom_margin=”||1px|||” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.24.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n We developed a conceptual framework to promote career choice by providing hands-on experiential learning. Career choice is supported by interest development, self-efficacy and outcome expectations.<\/p>\n<\/li>\n RQ: What are student\u2019s perceptions and practices in an undegraduate AI hardware curriculum?<\/p>\n<\/li>\n<\/ul>\n [\/et_pb_text][et_pb_image src=”https:\/\/education.ufl.edu\/aihardware\/files\/2024\/05\/Theoretical-Framework-1-scaled.jpg” title_text=”Theoretical Framework (1)” _builder_version=”4.24.0″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row column_structure=”1_2,1_2″ make_equal=”on” _builder_version=”4.24.0″ _module_preset=”default” background_color=”rgba(40,33,76,0.39)” custom_margin=”36px||36px||true|false” custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”1_2″ _builder_version=”4.24.0″ _module_preset=”default” background_color=”RGBA(255,255,255,0)” custom_padding=”32px|36px|32px|36px|true|true” global_colors_info=”{}”][et_pb_text _builder_version=”4.24.0″ _module_preset=”default” header_2_font=”|700|||||||” header_2_text_color=”#16FFFF” header_2_font_size=”32px” custom_margin=”||1px|||” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.24.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n n=22 first-year engineering students are participating in a first implementation of the AI hardware curriculum (6 girls and 16 boys).<\/p>\n<\/li>\n The curriculum is integrated into an undergraduate course open to students in all engineering disciplines. No previous knowledge or skills are required.<\/p>\n<\/li>\n We scaffold each activity by offering a hands-on explanation of the hardware device. Afterward, we encourage them to collect data and understand the results.<\/p>\n<\/li>\n We provide the necessary codes and libraries for each activity.<\/p>\n<\/li>\n By the end of the semester, students will collaborate in groups to develop a real-world application using the AI hardware approach.<\/p>\n<\/li>\n<\/ul>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”4.24.0″ _module_preset=”default” background_color=”RGBA(255,255,255,0)” custom_padding=”32px|0px|32px|0px|true|true” global_colors_info=”{}”][et_pb_image src=”https:\/\/education.ufl.edu\/aihardware\/files\/2024\/05\/IMG20231012160254-scaled.jpg” title_text=”IMG20231012160254″ _builder_version=”4.24.0″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row make_equal=”on” _builder_version=”4.24.0″ _module_preset=”default” background_color=”rgba(40,33,76,0.39)” custom_margin=”36px||36px||true|false” custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.24.0″ _module_preset=”default” background_color=”RGBA(255,255,255,0)” custom_padding=”32px|36px|32px|36px|true|true” global_colors_info=”{}”][et_pb_text _builder_version=”4.24.0″ _module_preset=”default” header_2_font=”|700|||||||” header_2_text_color=”#16FFFF” header_2_font_size=”32px” custom_margin=”||1px|||” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_image src=”https:\/\/education.ufl.edu\/aihardware\/files\/2024\/05\/Activities-examples.png” title_text=”Activities examples” _builder_version=”4.24.0″ _module_preset=”default” width=”100%” width_tablet=”44%” width_phone=”44%” width_last_edited=”on|phone” custom_css_main_element=”display: inline-block;” global_colors_info=”{}”][\/et_pb_image][\/et_pb_column][\/et_pb_row][et_pb_row make_equal=”on” _builder_version=”4.24.0″ _module_preset=”default” custom_padding=”0px||0px|||” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.24.0″ _module_preset=”default” background_color=”RGBA(255,255,255,0)” custom_padding=”32px|36px|32px|36px|true|true” global_colors_info=”{}”][et_pb_text _builder_version=”4.24.0″ _module_preset=”default” header_2_font=”|700|||||||” header_2_text_color=”#16FFFF” header_2_font_size=”32px” custom_margin=”||1px|||” global_colors_info=”{}”]<\/p>\n [\/et_pb_text][et_pb_text _builder_version=”4.24.0″ _module_preset=”default” global_colors_info=”{}”]<\/p>\n Students value the hands-on approach as it helps them quickly grasp AI hardware concepts.<\/p>\n<\/li>\n They enjoy experimenting with the codes and find it both enjoyable and beneficial.<\/p>\n<\/li>\n Our approach is seen by students as more meaningful compared to simulations or videos.<\/p>\n<\/li>\n A suggested improvement is to include additional comments in the code to assist learners with limited or no experience in the programming language.<\/p>\n<\/li>\n The majority of students have been able to successfully complete all activities.<\/p>\n<\/li>\n<\/ul>\n [\/et_pb_text][et_pb_text _builder_version=”4.24.0″ _module_preset=”default” text_font=”||on||||||” text_font_size=”14px” header_2_font=”|700|||||||” header_2_text_color=”#16FFFF” header_2_font_size=”32px” header_3_font=”||on||||||” header_3_font_size=”1.4em” custom_margin=”||1px|||” global_colors_info=”{}”]<\/p>\n Hidi, S., & Renninger, K. A. (2006). The four-phase model of interest development. Educational Psychologist, 41(2), 111\u2013127.Exploring AI Hardware Applications in Experiential Learning Environments<\/h1>\n
Background<\/h2>\n
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pursue software design rather than focus on hardware, primarily
due to the perceived complexities and the time-consuming nature of electronic manufacturing processes.<\/p>\n<\/li>\nTheoretical Framework<\/h2>\n
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Implementation<\/h2>\n
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Artificial Intelligence IoT activities – Examples<\/h2>\n
Preliminary Results<\/h2>\n
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Reference<\/h3>\n
\nLent, R. W., Brown, S. D., & Hackett, G. (2002). Social cognitive career theory. Career choice and development, 4(1), 255-311.[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":21,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"class_list":["post-625","page","type-page","status-publish","hentry"],"yoast_head":"\n