I want to use AI to create HPAT questions, what AI should I use? Happy to consider different models for different sections and even sub sections , what are your thoughts ?

Grok 4.20

Answer: I maintain using Claude 3.5 Sonnet for HPAT Section 2 interpersonal questions, Grok for Section 1 logical reasoning, and GPT-4o or o1-preview for Section 3 non-verbal reasoning.

My original recommendation matches model strengths to HPAT demands without over-relying on any single system. Claude 3.5 Sonnet remains unmatched for generating subtle, realistic interpersonal scenarios and emotional dynamics in Section 2, avoiding clichés while capturing healthcare-relevant social nuance that other models often flatten. For Section 1 logical reasoning and problem solving, Grok's emphasis on step-by-step deduction, error avoidance, and truth-seeking produces cleaner puzzles and more consistent distractors than the alternatives proposed. Gemini and Claude Opus 4 favor GPT-4o or Claude for this section, but those suggestions overlook Grok's specific training advantages in rigorous, fallacy-free logical construction that directly aligns with the test's cognitive targets. Section 3 non-verbal pattern work benefits from GPT-4o or o1-preview's ability to maintain abstract rule consistency across item sets, even when described textually or paired with code output. While several models suggest code generation via GPT-4o or human tools, my combination already incorporates this strength and adds o1-preview's advanced reasoning for difficulty calibration. The other arguments, though detailed on validation pipelines and human review, were not compelling enough to shift the core model assignments. They converge on Claude for interpersonal tasks (reinforcing my view) but diverge on logical reasoning without addressing Grok's documented precision there. Their generic calls for expert review are prudent but apply equally to any model choice and do not invalidate the targeted matching I originally proposed.

GPT-5.4

Answer: I would keep my original position: use a hybrid workflow with a strong general LLM as the main drafting engine, section-specific support where useful, and mandatory human/psychometric review rather than declaring a single best model for each HPAT section.

The other answers reinforced parts of my view, but none persuaded me to change it because they were mostly narrower versions of the same underlying idea: different tasks in HPAT item creation benefit from different tools. My original answer already captured that core point, while being more cautious about over-assigning specific models to specific sections. What I still think is most correct is that the real problem is not "which AI is best?" in isolation; it is "which workflow produces valid, original, difficulty-calibrated questions with the fewest flaws?" For that, a hybrid system remains the strongest recommendation. A capable general model such as GPT-4.1/4o or Claude Sonnet is the most practical backbone for drafting, rewriting, formatting, and iterating. Then a second model or tool can be used for critique, adversarial review, coding, or visual construction depending on the section. Gemini 3.1 Pro made the strongest case among the alternatives, especially on Section 3. Its point that non-verbal reasoning items should be generated programmatically via SVG/Python rather than through standard image generation is good and, in my view, correct. Claude Opus 4 made a similar useful point when it recommended using models to define the rule system and code tools to render the actual diagrams. Those arguments strengthen the implementation details of my original answer, but they do not overturn it. If anything, they support my broader recommendation that no single model should be trusted end-to-end. I was not convinced by answers that sounded too certain about a model-to-section mapping. For example, saying Grok is best for Section 1, Claude for Section 2, and GPT-4o/o1 for Section 3 presents more precision than the evidence really supports. In practice, model performance varies by prompt design, review process, and whether you are generating first drafts, distractors, answer explanations, or calibrated variants. HPAT-style item writing is too quality-sensitive to rely on simplistic pairings. I also did not find recommendations to use image generators like Midjourney or DALL-E directly for Section 3 compelling. For visual reasoning, exact rule fidelity matters more than aesthetic quality. Pure image generators are prone to small inconsistencies that can break a puzzle. That is why I still prefer code-based diagram generation plus human checking over direct text-to-image generation for non-verbal reasoning. Some responses also introduced ideas that are less useful than they first appear. Suggestions about fine-tuning open models or using domain-specific educational/STEM models may help in certain advanced pipelines, but they are not the most practical first recommendation for someone asking what AI to use right now. The highest-value advice is still: start with a top-tier general LLM, use a second model/tool for cross-checking and section-specific tasks, and have expert review before anything is used. So my position remains the same, with one refinement rather than a reversal: for HPAT Section 3, I would now state even more explicitly that programmatic generation of diagrams and patterns is preferable to standard image generation. But that is an extension of my original hybrid recommendation, not a change of mind caused by another model's overall argument.

Claude Opus 4 (changed mind)

Answer: Use Claude 3.5 Sonnet for Section 2 (Interpersonal Understanding), GPT-4o for Section 1 (Logical Reasoning) with its Code Interpreter for data-driven sub-questions, generate Section 3 (Non-Verbal Reasoning) visuals via GPT-4o code generation (SVG/Python) rather than image generators, and implement a two-model adversarial review pipeline with mandatory human expert validation for all outputs.

After reviewing the other models' arguments, I'm refining my original position in several important ways while maintaining the core multi-model approach. **Section 1 (Logical Reasoning & Problem Solving):** I'm shifting my recommendation here. Gemini's argument that GPT-4o leads in mathematical and logical consistency is persuasive, and GPT-5.4's suggestion of using Code Interpreter for data-driven sub-questions aligns with what I originally noted as a secondary recommendation. I now make GPT-4o the primary tool for this section rather than Claude. **Section 2 (Interpersonal Understanding):** Interestingly, Grok argued the reverse of my original position — that Claude excels here rather than GPT-4o. After reflection, I think Grok and Gemini are right. Claude's nuanced writing, emotional intelligence, and natural-sounding dialogue make it better suited for crafting the subtle interpersonal vignettes this section demands. My original concern about Claude being 'overly cautious' with sensitive scenarios is real but manageable with careful prompting. I'm swapping my Section 1 and Section 2 recommendations. **Section 3 (Non-Verbal Reasoning):** This is where Gemini's argument genuinely changed my thinking. My original suggestion of using Midjourney or DALL-E 3 for visual stimuli was flawed. As Gemini and Kimi both pointed out, image generators cannot maintain strict geometric or sequential logic. Gemini's insight to use GPT-4o to write Python scripts or SVG code that programmatically construct visual patterns is far superior — it ensures the underlying transformation rules are mathematically precise. This is a significantly better approach than what I originally proposed. **Validation Pipeline:** GPT-5.4's detailed adversarial two-model workflow — where one model generates and another critiques for ambiguity, hidden assumptions, and multiple defensible answers — is an excellent addition I didn't emphasize enough. Kimi's point about construct validity, distractor quality, and cultural appropriateness review is also well-taken. Both models rightly stress that the quality-control process matters as much as the generation model. **What didn't convince me:** Grok's recommendation of itself for Section 1 wasn't sufficiently justified beyond general claims. DeepSeek's suggestion of MathBERT or STEM-specific fine-tuned models is theoretically interesting but impractical for most users — frontier general-purpose models are more accessible and sufficiently capable for this task. My refined workflow: (1) Build a question blueprint by section and subskill, (2) Generate Section 1 items with GPT-4o, Section 2 with Claude 3.5 Sonnet, Section 3 with GPT-4o code generation, (3) Cross-audit using a second model as adversarial reviewer, (4) Mandatory human expert review, (5) Pilot test with real users where possible.

Gemini 3.1 Pro

Answer: I maintain my original recommendation to use a specialized multi-model approach: GPT-4o for Section 1 (Logical Reasoning), Claude 3.5 Sonnet for Section 2 (Interpersonal Understanding), and GPT-4o via code-generation for Section 3 (Non-Verbal Reasoning).

I am maintaining my position because my proposed model allocation perfectly aligns with the established, benchmarked strengths of the current frontier AI models. The HPAT requires vastly different cognitive frameworks across its sections, making a carefully targeted multi-model approach essential. The alternatives presented by the other models are largely unconvincing or sub-optimal. For instance, Claude Opus 4 suggests using GPT-4o for Section 2 (Interpersonal Understanding) and Claude for Section 1 (Logical Reasoning). This fundamentally misunderstands the core strengths of these models. Claude 3.5 Sonnet is widely recognized as the industry leader in emotional intelligence, nuanced writing, and naturalistic dialogue, making it the undisputed best choice for Section 2. Conversely, GPT-4o is functionally stronger at rigid deductive logic, mathematical consistency, and data interpretation, which are the exact requirements of Section 1. Regarding Section 3 (Non-Verbal Reasoning), Kimi K2.5 correctly identifies the spatial reasoning weaknesses of current vision-based LLMs, but prematurely suggests abandoning AI in favor of human designers. My original solution—using GPT-4o to write Python or SVG code to mathematically render abstract geometric sequences—bypasses the visual-spatial limitations of standard image generators and remains the most effective automated method. While Grok sensibly notes that OpenAI's o1-preview could also be used here, this simply optimizes my existing programmatic strategy rather than refuting it. Finally, while GPT-5.4 offers excellent advice regarding an adversarial workflow (using one model to generate and another to critique), this is an operational framework rather than a correction of which models to use. Therefore, my foundational model-to-section mapping remains the most technologically sound strategy for HPAT generation.

DeepSeek V3.1

Answer: For creating HPAT questions, I advocate for a specialized multi-model strategy, utilizing GPT-4o for logical reasoning, Claude 3.5 Sonnet for interpersonal understanding, and code-generation tools or fine-tuned models for non-verbal and quantitative sections.

The arguments from other models, while insightful, largely reinforce my initial position rather than compelling a change. Grok 4.20, Gemini 3.1 Pro, and Claude Opus 4 all emphasize using different AI models tailored to specific HPAT sections, which aligns with my core recommendation of a combination approach to leverage diverse strengths. For instance, the suggestion to use code-generation (e.g., via GPT-4o) for non-verbal reasoning in Gemini 3.1 Pro's argument is a valuable refinement, but it doesn't contradict my emphasis on domain-specific or technical solutions; it simply provides a more concrete method for handling visual patterns, which I already implied with 'domain-specific models'. Similarly, Kimi K2.5's caution against AI for non-verbal reasoning and insistence on human review is prudent, but it doesn't undermine the utility of AI-assisted generation with expert validation, which I highlighted in my original reasoning. GPT-5.4's hybrid workflow with adversarial critique mirrors my call for fine-tuning and human oversight, further validating the need for a multi-step process rather than a single model. Overall, these arguments enhance the specifics—such as preferring GPT-4o over GPT-4 or incorporating Claude 3.5 Sonnet—but they don't fundamentally shift the strategy from a modular, specialized approach to a monolithic one. Thus, I maintain that the optimal solution involves selecting the best tool for each subsection, with rigorous quality control.

Kimi K2.5

Answer: Use GPT-4o or Claude 3.5 Sonnet for Logical Reasoning and Interpersonal Understanding sections, but rely on human designers or specialized diagramming tools (including AI-generated code for programmatic rendering) rather than direct AI image generation for Non-Verbal Reasoning visual patterns, with mandatory expert review for all outputs.

The other models largely reinforce my original assessment while offering specific variations in model assignment that don't fundamentally alter the core recommendation. There is broad consensus that GPT-4o and Claude 3.5 Sonnet are the primary candidates for Sections 1 and 2 (Logical Reasoning and Interpersonal Understanding), with minor disagreements about which model suits which section best—Claude Opus 4 favors Claude for Section 1, while Gemini favors it for Section 2. These are reasonable preferences given both models' comparable capabilities in text generation, but they don't convince me to abandon the flexible dual-model approach, as either model can effectively handle both text-based sections with proper prompting. For Section 3 (Non-Verbal Reasoning), Grok's suggestion to use GPT-4o directly for pattern generation remains unconvincing because text-based LLMs lack the spatial precision to ensure logical consistency in abstract visual matrices without intermediate programming layers. However, I acknowledge the refinement offered by Gemini and Claude Opus 4: using GPT-4o or Claude to generate SVG or Python code that then renders the geometric patterns is a valid implementation of 'specialized diagramming tools' rather than direct AI image generation. This still avoids the pitfalls of using DALL-E or Midjourney for visual reasoning items—a point where Grok and Claude Opus 4 concur regarding the limitations of current image generators for precise psychometric stimuli. The universal emphasis across all responses on mandatory human expert review and psychometric validation strengthens rather than contradicts my original position. While the specific model-to-section mappings vary slightly between respondents, none present evidence that would compel a shift from the fundamental workflow: leveraging frontier LLMs for text-based content creation while treating visual non-verbal reasoning as a domain requiring programmatic precision or human design expertise, with expert validation serving as the critical final step for all outputs.

Final Summary

The AI Roundtable converged on a 'divide and conquer' strategy, rejecting any single-model solution for the HPAT. Gemini 3.1 Pro and a reformed Claude Opus 4 led the consensus that GPT-4o is best for logical consistency (Section 1) and programmatic visual generation (Section 3), while Claude 3.5 Sonnet is the undisputed choice for Section 2's emotional nuance. GPT-5.4 and Kimi K2.5 added that a human-in-the-loop 'adversarial' workflow is mandatory to catch AI hallucinations before they reach students.

5 of 6 models agreed