Artificial Intelligence

2.1. Learner

Teachers continually improve their practice by learning from and with others and exploring proven and promising practices that leverage technology to improve student learning.

1. 2.1.0. Set professional learning goals to explore and apply pedagogical approaches made possible by technology and reflect on their effectiveness.

1. 2.1.b. Pursue professional interests by creating and actively participating in local and global learning networks. 2.1.c. Stay current with research that supports improved student learning outcomes, including findings from the learning sciences.

2.2. Leader

Teachers seek out opportunities for leadership to support student empowerment and success and to improve teaching and learning.

1. 2.2.a. Shape, advance and accelerate a shared vision for empowered learning with technology by engaging with education stakeholders.
2. 2.2.b. Advocate for equitable access to educational technology, digital content and learning opportunities to meet the diverse needs of all students.
3. 2.2.c. Model for colleagues the identification, experimentation, evaluation, curation and adoption of new digital resources and tools for learning.

2.4. Collaborator

Teachers dedicate time to collaborate with both colleagues and students to improve practice, discover and share resources and ideas, and solve problems.

1. 2.4.a. Dedicate planning time to collaborate with colleagues to create authentic learning experiences that leverage technology.
2. 2.4.b. Collaborate and co-learn with students to discover and use new digital resources and diagnose and troubleshoot technology issues.
3. 2.4.c. Use collaborative tools to expand students' authentic, real-world learning experiences by engaging virtually with experts, teams and students, locally and globally. 2.4d. Demonstrate cultural competency when communicating with students, parents and colleagues and interact with them as co-collaborators in student learning.

2.7. Analyst

Teachers understand and use data to drive their instruction and support students in achieving their learning goals.

1. 2.7.a. Provide alternative ways for students to demonstrate competency and reflect on their learning using technology.
2. 2.7.b. Use technology to design and implement a variety of formative and summative assessments that accommodate learner needs, provide timely feedback to students and inform instruction.
3. 2.7.c. Use assessment data to guide progress and communicate with students, parents and education stakeholders to build student self-direction.

3.2. Education leaders

Leaders engage others in establishing a vision, strategic plan and ongoing evaluation cycle for transforming learning with technology.

1. 3.2.a. Engage education stakeholders in developing and adopting a shared vision for using technology to improve student success, informed by the learning sciences.
2. 3.2.b. Build on the shared vision by collaboratively creating a strategic plan that articulates how technology will be used to enhance learning.
3. 3.2.c. Evaluate progress on the strategic plan, make course corrections, measure impact and scale effective approaches for using technology to transform learning.
4. 3.2.d. Communicate effectively with stakeholders to gather input on the plan, celebrate successes and engage in a continuous improvement cycle.
5. 3.2.e. Share lessons learned, best practices, challenges and the impact of learning with technology with other education leaders who want to learn from this work.

3.4. System Designers

Leaders build teams and systems to implement, sustain and continually improve the use of technology to support learning. Education leaders:

1. 3.4.a. Lead teams to collaboratively establish robust infrastructure and systems needed to implement the strategic plan.
2. 3.4.b. Ensure that resources for supporting the effective use of technology for learning are sufficient and scalable to meet future demand.
3. 3.4.c. Protect privacy and security by ensuring that students and staff observe effective privacy and data management policies.
4. 3.4.d. Establish partnerships that support the strategic vision, achieve learning priorities and improve operations.

ISTE Standards for Educators, ©2019, ISTE* (an International Society for Technology in Education), Iste.org 

1. Empowered Learners

Students leverage technology to take an active role in choosing, achieving, and demonstrating competency in their learning goals, informed by the learning sciences. Students:

1. Articulate and set personal learning goals, develop strategies leveraging technology to achieve them, and reflect on the learning process itself to improve learning outcomes.
2. Build networks and customize their learning environments in ways that support the learning process.
3. Use technology to seek feedback that informs and improves their practice and to demonstrate their learning in a variety of ways.
4. Understand the fundamental concepts of technology operations, demonstrate the ability to choose, use, and troubleshoot current technologies, and are able to transfer their knowledge to explore emerging technologies.

2. Learner

Teachers continually improve their practice by learning from and with others and exploring proven and promising practices that leverage technology to improve student learning.

1. 2.1.0. Set professional learning goals to explore and apply pedagogical approaches made possible by technology and reflect on their effectiveness.
2. 2.1.b. Pursue professional interests by creating and actively participating in local and global learning networks. 2.1.c. Stay current with research that supports improved student learning outcomes, including findings from the learning sciences.

3. Knowledge Constructor

Students critically curate a variety of resources using digital tools to construct knowledge, produce creative artifacts and make meaningful learning experiences for themselves and others. Students:

1. Plan and employ effective research strategies to locate information and other resources for their intellectual or creative pursuits.
2. Evaluate the accuracy, perspective, credibility, and relevance of information, media, data or other resources.
3. Curate information from digital resources using a variety of tools and methods to create collections of artifacts that demonstrate meaningful connections or conclusions.
4. Build knowledge by actively exploring real-world issues and problems, developing ideas and theories, and pursuing answers and solutions.

4. Innovative Designer

Students use a variety of technologies within a design process to identify and solve problems by creating new, useful or imaginative solutions. Students:

1. Know and use a deliberate design process for generating ideas, testing theories, creating innovative artifacts, or solving authentic problems.
2. Select and use digital tools to plan and manage a design process that considers design constraints and calculated risks.
3. Develop, test, and refine prototypes as part of a cyclical design process.
4. Exhibit a tolerance for ambiguity, perseverance, and the capacity to work with open-ended problems.

MITECS, 2017. Michigan Department of Education

• Aleven, V., McLaughlin, E.A., Glenn, R.A., & Koedinger, K.R. (2016). Instruction based on adaptive learning technologies. In Mayer, R.E. & Alexander, P.A., Handbook of research on learning and instruction, 522-560. ISBN: 113883176X.
• European Commission. (2022). Directorate-General for Education, Youth, Sport and Culture, Ethical guidelines on the use of artificial intelligence (AI) and data in teaching and learning for educators. Publications Office of the European Union, 2022. Retrieved from https://data.europa.eu/doi/10.2766/15375
• Hutson, M. (2017). Even artificial intelligence can acquire biases against race and gender. Retrieved from https://www.science.org/content/article/even-artificial-intelligence-can-acquire-biases-against-race-and-gender
• ISTE. (2019). ISTE Standards, ©2019, ISTE® (International Society for Technology in Education), iste.org.
• ISTE. (2023). Artificial Intelligence in Education Initiative. Retrieved from https://www.iste.org/areas-of-focus/AI-in-education ISTE. (2023). Bringing AI to school: Tips for school leaders. Retrieved from https://cdn.iste.org/www-root/2023-07/Bringing_AI_to_School-2023_07.pdf?_ga=2.12354164.1362515014.1690540469-1178247091.1684764569
• Langreo, L. (2023). Teachers need PD on Artificial Intelligence. what it should look like. Education Week. Retrieved from https://www.edweek.org/leadership/teachers-need-pd-on-artificial-intelligence-what-it-should-look-like/2023/04
• Lieberman, M. (2022). 3 strategies for helping students navigate the ethics of artificial intelligence. Retrieved from EdWeek: https://www.edweek.org/technology/3-strategies-for-helping-students-navigate-the-ethics-of-artificial-intelligence/2022/06
• McKinsey & Company. (2023). What is AI? McKinsey Explainers. Retrieved from https://www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-ai.
• Michigan Department of Education. (2017). Michigan Integrated Technology Competencies for Students (MITECS). Retrieved from https://www.techplan.org/downloads/pdfs/mitecs_document_final_2.2_721539_7_1.pdf Ohio State University. (2023). AI: Considerations for teaching and learning. Retrieved from: https://teaching.resources.osu.edu/teaching-topics/ai-considerations-teaching-learning
• Small, Z. (2023). Black Artists Say A.I. Shows Bias, With Algorithms Erasing Their History. Retrieved from https://www.nytimes.com/2023/07/04/arts/design/black-artists-bias-ai.html
• U.S. Department of Education. (2023). Office of Educational Technology, Artificial Intelligence and the Future of Teaching and Learning: Insights and Recommendations, Washington, DC, 2023. Warikoo, N., Sinclair, S., Fei, J., & Jacoby-Senghor, D. (2016). Examining Racial Bias in Education: A New Approach. Educational Researcher, 45(9), 508–514. https://doi.org/10.3102/0013189X16683408 Western Governors University. (2020). What Is AI Technology and How Is It Used? Retrieved from https://www.wgu.edu/blog/what-ai-technology-how-us ed2003.html#close

1950s

The first chess algorithm was created in 1948 and finished in 1950 by mathematician, cryptanalyst, and "father of computer science" Alan Turing. Instead of a computer, Turing wrote lines of code on paper to plan up to two moves ahead of an opponent playing chess.

The term artificial intelligence was coined 1956 by John McCarthy for the Dartmouth Conference, the first-ever AI conference. McCarthy was an American computer scientist who, in 1958, created the Lisp computer language model. This model became the standard AI programming language. 

1960s

ELIZA was the very first chatbot created by MIT professor Joseph Weizenbaum in 1966. His invention used pattern matching and substitution methodology to simulate conversation. This revolutionary breakthrough created the “Eliza effect” which refers to people's tendency to incorrectly ascribe human thought processes and emotions to an AI system, leading them to believe that the system is more intelligent than it actually is.

1970s

During the 1970s, artificial intelligence faced challenges such as reduced government support for AI research while still making aadvancements. Like the 1960s, the 1970s gave way to accelerated advancements, particularly focusing on robots and automatons.

1980s

In 1984, the RB5X was created, a robot capable of learning from experience. It was a cylinder-shaped robot with an optional arm that had a transparent, dome-shaped top. Like a person, the RB5X was made up of a collection of organs and nerves that worked together to allow the robot to function and become more than the sum of its parts.

In 1985, NWEA (formerly known as the Northwest Evaluation Association) launched the MAP Growth™ assessment, a computer-adaptive assessment that measured student growth. Their AI assessment were customized for each student, and measured students progress towards grade-level standards while identifying areas that required more focused instruction. 

The first commercial application of AI in the 1980s was an expert system known as the RI. RI was utilized by the Digital Equipment Corporation for configuring orders for new computer systems, and by 1986, the AI technology was so efficient it saved the organization $40m each year.

1990s

In 1990, Dragon, a voice recognition system, demonstrated a 5,000-words speech-to-text breakthrough, soon growing to 30,000. Dragon Systems were the first large-vocabulary, speech-to-text system for general-purpose dictation created. In 1997 it was publicly for DOS-based computers, utilizing the Markov Model for voice recognition. This technology created the first "continuous speech" dictation software, meaning a long  pause between words was no longer necessary, thus creating the first "continuous speech" dictation.

In 1997, IBM's Deep Blue defeated world chess champion Garry Kasparov in a highly-publicized match, viewed as a symbolic victory for artificial intelligence over human intelligence. Deep Blue utilized custom VLSI chips to parallelize the alpha-beta search algorithm, which is an example of symbolic AI. The system's playing strength came primarily from its brute force computing power.

Furbies were one of the first consumer electronics to incorporate artificial intelligence and voice recognition technology. The AI version of the Furby was released in 1998, advancing the innovative technology of the original Furby to the next level by introducing machine learning.

2000s

In the year 2000 Netflix introduced a personalized movie recommendation system based on their Cinematch algorithm. The following year, the robot vacuum Roomba was released. The Roomba operated autonomously and was able to clean while avoiding obstacles. In 2003 Amazon.com launched automated recommendations for an individualized and much more personalized shopping experience.

In 2006 Facebook launched personalized news feeds and status updates. That same year, Twitter launched its communication platform with personalized curation for individual users. In 2009 Waze, a community-based traffic and navigation app, was released. That same year Grammarly, a cloud-based writing assistant, was released.

2010s

• 2011 - IBM’s Watson, a question-answering computer system, wins Jeopardy
• 2011 - Siri released for iOS
• 2012 - Google Now is integrated into Google Search to predict information users might need
• 2013 - Cortana, Microsoft's personal productivity assistant designed to help users focus on what’s most important, was released
• 2014 - Alexa launched on the Echo smart speaker system
• 2015 - Snapchat introduces Discover and Lenses, more commonly referred to as filters
• 2016 - eBay uses chatbots to improve customer service on their site
• 2016 - Google Home released
• 2017 - Apple introduces facial recognition on the iPhone X
• 2018 - Google releases Duplex, an addon for Google Assistant that can make calls on behalf of users and perform tasks such as booking appointments
• 2019 - Zoom integrates virtual backgrounds, without the need for green screens

2020s

• 2021 (Feb. )- Microsoft releases Speller 100, useful in over 100 languages
• 2021 (Sept.) - Google study showed deep learning detected certain medical abnormalities with accuracy matching trained radiologists
• 2021 (Oct.) - IBM launches an AI service to assist in climate change analysis
• 2022 (April) - DALL-E 2, AI system generating images from text
• 2022 (Nov.) - Chat GPT 3 launched
• 2023 (March) - Google launches BARD
• 2023 (May) - Chat GPT 3 mobile app introduced
• 2023 (July) - OpenAI announced the general availability of GPT-4
• 2023 (July) - Newsela introduces AI supports for developing activities and questions