Tailored outcomes powered by AI: Simply communicate your intentions and research objectives to generate precisely fitting codes for your research – effortlessly. Accessible now on ATLAS.ti Desktop and Web.

Unlock the Power of Intentional AI Coding with ATLAS.ti

Increased control, enhanced transparency, and superior outcomes await with our latest innovation. Introducing the world’s inaugural Intentional AI Coding solution, now accessible on both ATLAS.ti Desktop and Web platforms. Powered by the cutting-edge technology of ChatGPT from OpenAI, our advanced AI coding assistant empowers you to direct automated coding precisely where you need it.

Intentional AI Coding can improve your research in the following ways

Guided Customization for Tailored Results

🧭 Bid farewell to generic outcomes. With Intentional AI Coding, you take the reins, providing explicit instructions and research goals to generate codes perfectly aligned with your intentions. By furnishing additional context, you ensure precise and relevant suggestions customized to your unique research requirements.

Enhanced Transparency and Scientific Rigor

👁️ Ensure the integrity of your research process with Intentional AI Coding. You may promote transparency and traceability from produced codes back to your research process and aims by communicating your goals to the AI. This meticulous approach upholds scientific rigor while facilitating seamless alignment with your research objectives.

Empowering Human Oversight

🕹️ Maintain control every step of the way. Intentional AI Coding enables you to review, refine, and customize research questions and code categories before application. The final codes remain entirely yours, keeping you firmly in control of your research outcomes.

Unlock Deeper Insights with Unprecedented Speed

⚡ Experience accelerated analysis and uncover profound insights faster than ever before. With improved precision from the outset, identify themes and draw robust conclusions swiftly and reliably

Please note: Intentional AI Coding is currently in beta phase and available exclusively for ATLAS.ti Desktop and Web. While AI responses may occasionally be inaccurate and response times slower during this phase, we are constantly working to enhance performance.

AI Privacy Mode for Enhanced Confidentiality

We understand the importance of privacy, especially for sensitive projects. Activate AI Privacy Mode with a simple switch to ensure that no data is submitted externally, giving you peace of mind and complete control over your research process.

User Control: Activate AI Privacy Mode within the ATLAS.ti app to safeguard your data and privacy, with no external submissions.

Admin Control for Multi-User Licenses: Administrators can manage this feature at an organizational level, ensuring uniform standards and privacy across all users.

Conversational AI: Transforming Data Interaction

Available in ATLAS.ti Web, our enhanced Conversational AI feature revolutionizes data analysis by enabling natural conversation to explore, clarify, and distill key insights from your documents.

New! Multi-Document Conversations for Expanded Analysis

Engage with multiple documents simultaneously, expanding your analytical scope and capabilities. Start conversing with your data like never before, querying and comparing across various documents seamlessly within one dialogue.

Transform your research experience with Intentional AI Coding and enhanced Conversational AI in ATLAS.ti Desktop and Web. Unlock the full potential of your data analysis today.

FAQs

1. What is AI coding in Atlas TI?

AI coding in ATLAS.ti refers to the use of artificial intelligence tools such as AI Coding, AI Suggested Codes, AI Summaries, and Conversational AI, which enable users to swiftly and effectively comprehend and categorize their data. This allows users to allocate more time to the essential aspects of data analysis.

2. What are the 7 steps in coding and analyzing data in content analysis?

The process of coding and analyzing data in content analysis typically involves the following steps:

1. Data Preparation
2. Determining the Unit of Analysis
3. Creating Categories and Coding Scheme
4. Pilot Testing the Coding Scheme on a Text Sample
5. Coding the Entire Dataset
6. Evaluating Coding Consistency
7. Drawing Conclusions from Coded Data
8. Documenting Methods and Findings

3. Has learning Atlas.ti been straightforward for users?

According to feedback, many find it easy to use and appreciate its robust features for conducting advanced analyses. In a user survey conducted in 2022, it was revealed that 97% of users appreciated the ease of use provided by ATLAS.ti.

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