AI Developer Diploma: Difference between revisions

Revision as of 12:53, 22 February 2026

The AI Developer Diploma trajectory prepares students for entry-level work as an AI-assisted software developer. We follow California's Software and Systems Development high school model curriculum standards, with emphasis on the Intelligent Computing Standards as well the O*NET competency requirements, to ensure that graduates have the qualifications for a job in their home market or offshored in a more developed economy. Of most importance, this learning stack has AI built into its DNA, not only for learning better, but for using it as part of your coding, where employers now expect their programmers to use vibe coding tools to be more productive. But at the same time, want quality code, not "AI Slop".

Three Forms of AI Development

Students will become competent in three different forms of AI development:

Prompt Engineering

This form of AI development is becoming expected in the workforce marketplace in all industries. Indeed, this is a distinguishing competency for being hired when others are not.

AI-Assisted Software Development

A specialized form of prompt engineering, is how to effectively use AI to assist in coding, or to have it do full vibe-coding. New developers seeking employment will struggle to compete if they have not mastered this competency, which revolves around good coding practices.

Machine Learning Development

The most challenging form of AI development is the development of AI itself via forms of machine learning and/or data science. While graduates will not be experts in this, they will have the statistical and thinking foundations for starting an AI major in university.

Competencies

You will develop sufficient competencies to be prepared for entry-level AI-assisted software development careers in the following topics:

Software Development Processes (waterfall, agile, and AI-agile)^C1.0
Determining Software Requirements (waterfall, agile, and AI-agile)^C2.0
Technology Stacks^C7.0
Computer Programming (in one or mor language based on your desired job)^C4.0
Testing, Debugging, and Improving Software^C5.0
User Interface Development^C3.0
Databases^C8.0
Hardware^C9.0
AI^C10.0

Developing Your Competencies

The following learning activities will be used to develop each of these competencies:

Participate in individualized competency cubes that are "taught" by a GPTeacher.
Participate in simulated work activities in each of these topics
Contribute to open source development to build a portfolio of work to prove your capabilities to employers

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 The AI Developer Diploma trajectory prepares students for entry-level work as an AI-assisted software developer.  We follow California's [https://www.cde.ca.gov/ci/ct/sf/documents/infocomtech.pdf#page=22 Software and Systems Development] high school model curriculum standards, with emphasis on the Intelligent Computing Standards as well the O*NET competency requirements, to ensure that graduates have the qualifications for a job in their home market or offshored in a more developed economy.  Of most importance, this learning stack has AI built into its DNA, not only for learning better, but for using it as part of your coding, where employers now expect their programmers to use vibe coding tools to be more productive.  But at the same time, want quality code, not "AI Slop".
+=== Three Forms of AI Development ===
+Students will become competent in three different forms of AI development:
+==== Prompt Engineering ====
+This form of AI development is becoming expected in the workforce marketplace in all industries. Indeed, this is a distinguishing competency for being hired when others are not.
+==== AI-Assisted Software Development ====
+A specialized form of prompt engineering, is how to effectively use AI to assist in coding, or to have it do full vibe-coding. New developers seeking employment will struggle to compete if they have not mastered this competency, which revolves around good coding practices.
+==== Machine Learning Development ====
+The most challenging form of AI development is the development of AI itself via forms of machine learning and/or data science.  While graduates will not be experts in this, they will have the statistical and thinking foundations for starting an AI major in university.
 === Competencies ===