Automated Scoring - Revision history

BPeat: /* Automated Compliance Scoring */

2024-03-16T19:25:05Z

‎Automated Compliance Scoring

BPeat at 19:21, 16 March 2024

2024-03-16T19:21:45Z

BPeat at 18:40, 16 September 2023

2023-09-16T18:40:42Z

BPeat at 18:22, 16 September 2023

2023-09-16T18:22:05Z

BPeat at 18:21, 16 September 2023

2023-09-16T18:21:51Z

BPeat at 18:15, 16 September 2023

2023-09-16T18:15:22Z

BPeat: /* Maximize Your Resume's Score */

2023-09-16T18:12:07Z

‎Maximize Your Resume's Score

BPeat: /* Maximize Your Resume's Score */

2023-09-16T18:05:16Z

‎Maximize Your Resume's Score

BPeat: /* How to Write A Proposal */

2023-09-16T18:01:53Z

‎How to Write A Proposal

BPeat at 18:00, 16 September 2023

2023-09-16T18:00:45Z

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 AI can be a valuable tool for scoring project submissions related to enterprise architecture alignment. Here's how:
-Analyzing Documentation:
+<b>Analyzing Documentation:</b>
-Keyword Matching: Train an AI model to identify keywords or phrases related to specific architectural artifacts, criteria, and mapping methods within the project documentation. This can flag submissions that lack crucial details or use incorrect terminology.
+<b>Code Analysis (if applicable):</b>
-Sentiment Analysis: AI can analyze the overall tone and clarity of the documentation. This can help identify submissions that are vague, poorly written, or lack confidence in their approach.
+* Pattern Recognition: If project submissions include code or configuration files, AI can be used to identify patterns that indicate adherence to architectural principles. This could involve looking for specific coding styles, library usage, or adherence to established design patterns.
-Pattern Recognition: If project submissions include code or configuration files, AI can be used to identify patterns that indicate adherence to architectural principles. This could involve looking for specific coding styles, library usage, or adherence to established design patterns.
+<b>Scoring and Feedback:</b>
-Scoring and Feedback:
+* Weighted Scoring: Assign different weights to various criteria based on their importance. The AI can then score submissions based on the presence and quality of these elements in the documentation or code.
 Overall, AI can be a powerful tool to streamline the initial evaluation of project submissions, but human expertise remains crucial for in-depth analysis and final decisions.

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 |title=PRIMO.ai
 |titlemode=append
-|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
+|keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Gemini, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools
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 = Automated Compliance Scoring =
-PROMPT TO BARD: Projects have to provide documentation on how they are using and mapping to the enterprise's architecture, the architecture has predefined criteria, architectural artifacts, how could ai be used to score the project's submission?
+PROMPT TO GEMINI: Projects have to provide documentation on how they are using and mapping to the enterprise's architecture, the architecture has predefined criteria, architectural artifacts, how could ai be used to score the project's submission?
-BARD'S RESPONSE:
+GEMINI'S RESPONSE:
-To score project submissions on enterprise architecture documentation using AI, you could follow these steps:
+AI can be a valuable tool for scoring project submissions related to enterprise architecture alignment. Here's how:
-* Identify the predefined criteria and architectural artifacts that the documentation should cover. This could involve working with your enterprise architecture team to identify the most important criteria and artifacts.
+Analyzing Documentation:
-Here are some specific examples of how AI could be used to score project submissions on enterprise architecture documentation:
+Keyword Matching: Train an AI model to identify keywords or phrases related to specific architectural artifacts, criteria, and mapping methods within the project documentation. This can flag submissions that lack crucial details or use incorrect terminology.
-* Identify the architectural artifacts that are present in the documentation. This could be done using a combination of natural language processing (NLP) and machine learning techniques.
+Pattern Recognition: If project submissions include code or configuration files, AI can be used to identify patterns that indicate adherence to architectural principles. This could involve looking for specific coding styles, library usage, or adherence to established design patterns.
-* Assess the completeness and accuracy of the documentation. This could be done by comparing the documentation to the predefined criteria and architectural artifacts.
+Scoring and Feedback:
-By using AI to score project submissions on enterprise architecture documentation, you can automate the scoring process, improve the consistency and accuracy of the scoring, and provide more timely and helpful feedback to the project teams. Here are some additional tips for using AI to score project submissions on enterprise architecture documentation:
+Weighted Scoring: Assign different weights to various criteria based on their importance. The AI can then score submissions based on the presence and quality of these elements in the documentation or code.
-* Use a high-quality training dataset. The quality of the training dataset will have a significant impact on the performance of the AI model. Make sure that the training dataset includes a variety of documentation submissions from different projects.
+Training Data: The effectiveness of AI for scoring relies heavily on the quality of the training data. This data should include well-documented, high-scoring projects that exemplify good architectural alignment.
-* Choose the right machine learning algorithm. There are a variety of different machine learning algorithms that can be used to train the AI model. Choose an algorithm that is well-suited to the type of data that you are using and the task that you are trying to accomplish.
+Human Oversight: AI should not replace human judgment entirely. A human reviewer should examine flagged submissions and the AI's feedback to ensure accuracy and catch nuances AI might miss.
-* Evaluate the AI model. Once you have trained the AI model, it is important to evaluate its performance on a held-out test set. This will help you to identify any areas where the model needs improvement.
+Bias Mitigation: Be mindful of potential biases in the training data that could skew the AI's scoring. Regularly review and adjust the training data to ensure fairness and alignment with evolving architectural principles.
-* Monitor the AI model over time. The performance of the AI model may degrade over time as the enterprise architecture changes. It is important to monitor the performance of the model and to retrain it as needed.
+Overall, AI can be a powerful tool to streamline the initial evaluation of project submissions, but human expertise remains crucial for in-depth analysis and final decisions.
 = How to Write A Proposal =

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 * [[Case Studies]]
-** [[Human Resources (HR)]]
+** [[Human Resources (HR)]] ... Write a Resume optimized for Applicant Tracking Systems (ATS)
 ** [[Education]]
 * [[Strategy & Tactics]] ... [[Project Management]] ... [[Best Practices]] ... [[Checklists]] ... [[Project Check-in]] ... [[Evaluation]] ... [[Evaluation - Measures|Measures]]
 * [[Large Language Model (LLM)]] ... [[Natural Language Processing (NLP)]]  ...[[Natural Language Generation (NLG)|Generation]] ... [[Natural Language Classification (NLC)|Classification]] ...  [[Natural Language Processing (NLP)#Natural Language Understanding (NLU)|Understanding]] ... [[Language Translation|Translation]] ... [[Natural Language Tools & Services|Tools & Services]]
 * [https://en.wikipedia.org/wiki/Automated_essay_scoring Automated essay scoring | Wikipedia]
 * [https://www.rapidparser.com/ CV Parsing with RapidParser]

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 * [https://www.rapidparser.com/ CV Parsing with RapidParser]
 * [https://www.ets.org/Media/Research/pdf/RR-04-45.pdf Automated Essay Scoring With E-rater® v.2.0 2005]
 = What is Automated Scoring =

← Older revision		Revision as of 18:21, 16 September 2023
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	* [https://www.rapidparser.com/ CV Parsing with RapidParser]		* [https://www.rapidparser.com/ CV Parsing with RapidParser]
	* [https://www.ets.org/Media/Research/pdf/RR-04-45.pdf Automated Essay Scoring With E-rater® v.2.0 2005]		* [https://www.ets.org/Media/Research/pdf/RR-04-45.pdf Automated Essay Scoring With E-rater® v.2.0 2005]
		+
		+	= What is Automated Scoring =
		+
		+	Automated scoring with AI is a process of using artificial intelligence (AI) to score submissions, such as essays, exams, and code. Overall, AI is a powerful tool that can be used to automate the scoring of submissions in a variety of settings. AI-powered scoring can help to save time, improve efficiency, and reduce bias. AI can be used to score submissions in a variety of ways, including:
		+
		+	* <b>Identifying keywords and phrases</b>: AI can be used to identify keywords and phrases that are relevant to the topic of the submission. This can be helpful for scoring essays and exams, where the goal is to assess the student's understanding of the material.
		+	* <b>Identifying patterns</b>: AI can be used to identify patterns in the submission. This can be helpful for scoring code, where the goal is to assess the student's ability to write correct and efficient code.
		+	* <b>Comparing submissions to a reference set</b>: AI can be used to compare submissions to a reference set of high-quality submissions. This can be helpful for scoring essays and exams, where the goal is to assess the student's writing skills and knowledge of the material.
		+
		+	AI can be used to score highly technical submissions by being trained on a large dataset of high-quality submissions. This dataset should include submissions from a variety of students and should cover a wide range of topics. Once the AI model is trained, it can be used to score new submissions by comparing them to the reference set of high-quality submissions. AI can also be used to identify noncompliance in submissions. For example, AI can be used to identify plagiarism, code that does not compile, and essays that do not meet the required word count. AI can also be used to identify submissions that contain harmful or offensive content.
		+
		+	Here are some examples of how AI is being used in automated scoring today:
		+
		+	* <b>Educational institutions</b>: Many educational institutions are using AI to score student essays and exams. This can help to free up teachers' time so that they can focus on other tasks, such as lesson planning and grading student work.
		+	* <b>Companies</b>: Some companies are using AI to score job applications and resumes. This can help to identify qualified candidates more quickly and efficiently.
		+	* <b>Government agencies</b>: Some government agencies are using AI to score applications for grants and other programs. This can help to ensure that the most deserving applicants are awarded funding.
		+

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-= Maximize Your Resume's Score =
+= Write a Resume optimized for Applicant Tracking Systems (ATS) =
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← Older revision		Revision as of 18:05, 16 September 2023
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 == Grant ==

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 * [https://www.ets.org/Media/Research/pdf/RR-04-45.pdf Automated Essay Scoring With E-rater® v.2.0 2005]
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 <b>Automated Scoring FAQs: how the technology works
 </b><br>Dr. Alistair Van Moere, Director of Test Development, Pearson, Knowledge Technologies, explains how the state-of-the-art Versant patented [[Speech Recognition]] technology is applied to the English language proficiency assessment
 |}
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