[new academics!!!]:Artificial Intelligence Prompt Linguistics (AIPL)

The Charter of Artificial Intelligence Prompt Linguistics (AIPL)

1. IntroductionIn recent years, humanity has continuously strived to elicit optimal outcomes from artificial intelligence through dialogue. However, these outcomes are fundamentally determined by the nature of the inputs we, as humans, provide. Rather than abdicating all cognitive tasks to AI, we utilize human language as the primary medium of interaction; consequently, our own linguistic competence must naturally be heightened.Nevertheless, because the underlying cognitive mechanisms of humans and AI are inherently different, it is impossible to uniformly dictate a single, definitive method of input. This necessitates a completely new branch of linguistics—one that unravels the intricate relationship between human linguistic inputs and the outputs generated by the quasi-intelligence of AI. Therefore, to systematize this domain, I hereby declare the establishment of this new academic discipline, Artificial Intelligence Prompt Linguistics (AIPL), and present its official charter below.
2. DefinitionArtificial Intelligence Prompt Linguistics (AIPL) is an academic discipline dedicated to illuminating the input-output relationship between humans and artificial intelligence through "prompts." By investigating the structures and governing laws of the language and prompts directed toward AI, the field seeks to maximize the accuracy and deterministic reliability of AI outputs.
3. Object of StudyThis discipline focuses on conversational artificial intelligence models widely utilized in contemporary society as its primary object of study. Representative models include Gemini, ChatGPT, Claude, Manus, Copilot, and DeepSeek.In particular, the core analytical focus is placed on the content and stylistic form of the prompts transmitted to these models. While modern AI models primarily interact via chat interfaces, this field intentionally isolates the prompt from system-level configurations or pre-settings. It investigates to what extent a single, initial input line can elicit highly precise and deterministic outputs, thereby ensuring strict fairness and variable control in empirical experiments.Readily admitting that iterative multi-turn dialogues can unveil distinct response behavioral tendencies, the discipline establishes two contrasting yet complementary subfields for comparative research:Artificial Intelligence Prompt Linguistics Without Prerequisites (AIPL-WoP): A subfield that examines the pure linguistic constraints and structural impacts of the "initial input line" alone, completely isolated from prior context or system configurations.Artificial Intelligence Prompt Linguistics With Prerequisites (AIPL-WP): A subfield that analyzes the adaptive response tendencies and linguistic evolution of AI through continuous dialogue, context accumulation, or pre-established settings.
4. MethodologyThe fundamental methodology of this discipline relies on contrastive empirical experiments, injecting diverse prompt variables into AI models and analyzing the resulting linguistic variations and behaviors. Due to the probabilistic nature of Large Language Models (LLMs), identical prompts can yield fluctuating responses. To guarantee empirical replicability and rigorous variable control, this field mandates a multi-trial sampling approach, executing a single prompt multiple times to capture the underlying statistical tendencies and response distributions.Prompts are classified into two distinct operational categories based on the structural constraints of the requested task, serving as the basis for comparative validation:Low-Latitude Prompts (Low-Latitude Imperatives): Refers to tasks where the underlying informational content is predetermined, such as text summarization, data extraction, or translation. Research in this category investigates how adjusting the "analytical perspectives or dimensions" within the prompt syntax alters the semantic precision and quality of the AI's output.High-Latitude Prompts: Refers to open-ended tasks requiring zero-shot ideation, opinion generation, or argument formulation. Research here explores not only the assignment of perspectives but also the structural formatting of constraints and "reward-centric prompts" (e.g., conditioning the AI's attention mechanism through reinforcement-like token phrasing or motivational stimuli), analyzing how these diverse linguistic strategies shift the boundaries of the generated content.By conducting systematic comparative analyses between Low-Latitude and High-Latitude prompts, this methodology aims to formalize the universal linguistic principles required to optimally control AI systems.
5. Research Ethical GuidelinesAll research and empirical experiments conducted within this academic discipline must strictly adhere to the following ethical guidelines:Ensuring Linguistic and Cultural Fairness (Isolation of Regional Contexts): This discipline investigates the scientific characteristics and behavioral tendencies of "AI responses (outputs)" independent of the specific natural language utilized in the prompt. To maintain absolute cross-cultural objectivity, researchers must intentionally isolate and discount regional or localized cultural backgrounds during output evaluation, thereby guaranteeing global linguistic and cultural fairness.Prohibition of Malicious Control and Jailbreaking (Contribution to Safety): The core purpose of this field is to elevate the semantic accuracy, deterministic reliability, and conversational safety of AI models. Under no circumstances shall the theories or methodologies developed herein be utilized to bypass, breach, or compromise AI safety mechanisms (such as content filters or safe-search boundaries), nor shall they be applied to any malicious exploits or adversarial prompt engineering.Enrichment of Human Language Expression (Preservation of Intellectual Autonomy): While this field dissects the strict input-output dynamics between prompts and AI, its overarching, supreme objective is "to enrich and expand the horizons of human linguistic expression through dialogue with artificial intelligence." It is explicitly stated herein that this research is not intended to encourage humans to abdicate their cognitive faculties to AI or default to passive automation; rather, it aims to preserve and empower human intellectual and linguistic autonomy.