Pathways to Mastery: A Taxonomy of Player Progression Systems in Commercial Video Games

3.1 Skill-based progression

Skill-based progression refers to systems where advancement is tied to the player’s improvement regarding game mechanics. In these systems, progression reflects player learning, reaction, and strategic decision-making. While numerical upgrades or rewards can be present in this progression system, the emphasis is on skill. Skill-based progression is common in competitive and difficult games where challenge is a core part of the experience. Unlike RPGs where progression requires “grinding” (doing tasks multiple times to accumulate XP to get stronger), skill-based games require players to refine their abilities through trial and error.

• Typical genres: Platformers, fighting games, puzzle games, soulslikes, competitive shooters.

• Example games: Celeste, Street Fighter II, Dark Souls, Counter-Strike: Global Offensive, Super Meat Boy.

• Required skills (depending on the game): Creative problem-solving, logical reasoning, pattern recognition, enemy AI adaptation, tactical positioning, aiming accuracy, perfect dodging, timing, memorizing level layouts, and combos.

Table 1 shows the SWOT analysis of skill-based progression systems.

Based on the SWOT analysis, the following design guidelines were formed for skill-based progression systems:

• Introduce a gradual and adaptive difficulty curve;

• Deliver actionable feedback that supports learning;

• Ensure fair and consistent challenge scaling.

3.2 XP-based progression systems

XP-based progression is one of the most common progression systems in video games. In this case, players earn XP by completing certain actions. These can be combat encounters, exploration, quest completion, or other achievements. By accumulating XP, players can increase their level or rank. Consequently, they can improve their stats, learn new abilities, or receive expanded customization options. Unlike skill-based progression, XP-based systems create a numerical representation of growth. This approach can appeal to players who enjoy measurable progress that provides clear indicators of advancement. This type of progression is effective because it is similar to the Operant conditioning principles [2, 11, 12]: players are rewarded at regular intervals that reinforces dopamine-driven feedback. This cycle keeps players engaged and motivated to continue playing. This also makes this system effective in RPGs and live-service games.

• Typical genres: RPGs, strategy games, massively multiplayer online (MMO) games, live-service titles.

• Example games: Final Fantasy, Pokémon, The Division, World of Warcraft.

• Figure 1 shows a summary of XP-based progression systems. Note that not all XP sources and rewards are mentioned, only the most common ones are presented.

Table 2 presents the results of the SWOT analysis of XP-based progression systems.

Based on the SWOT analysis, the following design guidelines were formed for XP-based progression systems:

• Diversify XP sources to support different playstyles;

• Avoid grind loops that hinder pacing;

• Link XP gain to meaningful in-game choices.

3.3 Item-based progression systems

Item-based progression refers to player advancement through gathering, crafting, or upgrading items such as weapons, armor, or other types of tools. Instead of relying on XP accumulation or skill mastery, item-based progression grants power and new abilities through objects obtained in the world of the video game. Item-based progression uses reward anticipation and the loot drop dopamine effect. It also has variable reward systems, focuses on player agency, and motivates exploration due to hidden gear. Item-based progression follows random reinforcement schedules that make rewards exciting but unpredictable. Unlike XP-based progression, this type offers choices since players can decide which weapons to craft, upgrade, or prioritize.

• Typical genres: Action RPGs, survival games, looter shooters.

• Example games: Monster Hunter: World, The Legend of Zelda, Diablo II, Borderlands.

It should be also noted that this type of progression system can have subcategories:

• Tool-based progression: Players acquire key items that unlock new areas or abilities;

• Loot-based progression: Players acquire randomized gear, with rarity tiers and stat improvements;

• Crafting-based progression: Players gather materials and craft weapons, armor, and tools.

To better understand how such systems work, Figure 2 shows a summary of item-based progression systems.

Table 3 details the results of the SWOT analysis of item-based progression systems.

Based on the SWOT analysis, the following design guidelines were formed for item-based progression systems:

• Combine random and deterministic item acquisition;

• Offer meaningful differences between equipment types;

• Balance rarity, utility, and accessibility.

3.4 Narrative progression systems

In these types of progression systems, player advancement occurs through story events, character development, and branching decisions instead of numerical stats or skill mastery. Progress is measured by how far a player has moved through a game’s storyline rather than by XP or gear upgrades. Thus, these types of systems focus on story engagement and character-driven decisions. These systems are mainly common in RPGs and adventure games since choices shape the game world and influence the experience of the players. Due to this, many games also offer multiple paths in the story. Interaction with non-playable characters (NPCs) also affects relationships, quests, and even endings. These types of progression systems have emotional engagement and decision-making psychology. For example, branching choices make players feel like they are co-authors of the story. Meanwhile, meaningful decisions create stronger emotional connections with the characters in the games. These progression systems also enhance the curiosity of the players since they replay narrative-driven games to see different outcomes of the story.

• Typical genres: Story-driven RPGs, adventure games, visual novels.

• Example games: The Witcher 3: Wild Hunt, Life is Strange, Detroit: Become Human.

For an example of narrative storytelling, we should think of a hypothetical scenario which is illustrated in Figure 3.

A mysterious stranger arrives in the town of the player character. He proposes the following deal for the player: “Find an ancient magic scroll, then bring it back to me or face my wrath”. Depending on the choice of the player, the following can happen: if the player declines this offer, the stranger calls his army and they destroy the hometown of the player character, resulting in a bad ending. If the player accepts, a new questline starts. Then, the player finds the scroll, but it is guarded by an ancient dragon. The game allows the player to fight the dragon or negotiate with it, and both decisions can yield two outcomes. The player can either be victorious or defeated. These two outcomes can consequently save the player character’s town or result in its destruction, respectively. Naturally, this hypothetical scenario is simplified since these narrative decision trees can be much more complex in today’s video games, and more than two endings can be reached.

To better understand these types of progression systems, a SWOT analysis was conducted. Its results can be seen in Table 4.

Based on the SWOT analysis, the following design guidelines were formed for narrative progression systems:

• Ensure choices influence both the plot and game world;

• Balance nonlinearity with narrative coherence;

• Reflect player decisions through world and character responses.

3.5 Social progression systems

In the case of social progression systems, players advance through interactions with other players, reputation systems, multiplayer ranking, and community-driven achievements. Unlike the previous player progression systems, social progression is tied to the status and social influence of the player while engaging in cooperative or competitive tasks in the world of the game. These progression systems are most frequently found in multiplayer games, MMO games, and other, live-service titles. Here, the player’s growth is measured by their standing in the community. This standing can consist of ranking in competitive play or contribution to cooperative goals. Furthermore, unlike progression in single-player games, social progression persists beyond individual play sessions with evolving leaderboards and guild hierarchies.

• Typical genres: MMOs, competitive online games, live-service games.

• Example games: League of Legends, Fortnite, Call of Duty: Warzone, EVE Online.

Table 5 shows the SWOT analysis of social progression systems.

Based on the SWOT analysis, the following design guidelines were formed for social progression systems:

• Reward collaborative behavior and sportsmanship;

• Balance competition with inclusivity;

• Design transparent and fair progression metrics.

3.6 Hybrid progression systems

These types of progression systems combine two or more aforementioned progression types to create a more dynamic and engaging experience. Instead of relying on a single method of advancement, these systems offer multiple paths for player growth. Consequently, these allow individuals to engage with the game mechanics that suit their playstyle since diverse player preferences require flexible and layered progression systems [18, 19]. For example, based on player types, achievers engage with XP-based leveling, item rewards, and rank progression; explorers can benefit from open-ended progression systems; socializers prefer faction reputation, guild ranks, and social influence-based growth; and competitors focus on skill-based mastery and ranked leaderboards.

• Typical genres: Open-world games, modern RPGs, sandbox games.

• Example games: The Witcher 3 (Narrative + XP + Skill), Elden Ring (Skill + XP + Item + Narrative), Destiny 2 (Item + XP + Social), Final Fantasy VII (XP + Item + Narrative).

Table 6 presents the results of the SWOT analysis of hybrid progression systems.

Based on the SWOT analysis, the following design guidelines were formed for hybrid progression systems:

• Ensure synergy between different progression systems;

• Avoid feature overload by streamlining redundant mechanics;

• Let players prioritize progression paths that suit their playstyle.

4.1 AI-driven dynamic difficulty adjustment

Dynamic difficulty adjustment is one of the earliest and most impactful applications of AI in games. Rather than offering static difficulty levels selected at the outset of a game, DDA allows for in-the-moment recalibration of gameplay variables based on player performance metrics such as reaction time, accuracy, success-to-failure ratio, or input frequency. These adaptations may occur invisibly or be communicated explicitly through in-game feedback systems [20]. For example, in Resident Evil 4 (both in the original and its remake), the AI dynamically adjusts enemy density and item drops to maintain a balanced experience. Valve’s Left 4 Dead series created the “AI Director,” which modulates environmental tension, spawn rates, and item placements to maximize player immersion and team coordination [21].

Table 7 details the results of the SWOT analysis of AI-driven DDA.

AI-based DDA systems have been shown to increase retention and satisfaction in both novice and expert players [22]. However, the literature shows that balance is the key if the system reduces difficulty too aggressively, players may feel their skill is undervalued. Conversely, poorly tuned increases can lead to frustration. Player modeling techniques such as behavior trees, fuzzy logic, and Bayesian inference are often used to make DDA more robust. For instance, Yannakakis and Hallam demonstrate how real-time adaptation mechanisms can dynamically adjust gameplay parameters such as challenge and curiosity to enhance player engagement [23]. Their work on the Bug Smasher game showed that even a simple gradient-ascent-based controller and player interaction data (e.g., response time, pressure variance, and input frequency) could significantly improve the entertainment value of a game. Moreover, their results indicate that maladaptive parameter changes correlate with decreased player preference. This shows the importance of precision in DDA design.

Recent advancements in AI-driven DDA have highlighted its evolution from heuristic-based systems to personalized mechanisms that enhance player engagement. For instance, Rahimi et al. introduced a continuous reinforcement learning-based DDA approach in a visual working memory game [24]. This improved player experience and performance compared to traditional rule-based methods. Similarly, Kristensen et al. used factorization machines to predict personalized game difficulty that enabled more accurately customized challenges to individual players [25]. Furthermore, adaptive systems using Monte Carlo Tree Search (MCTS) algorithms have been developed to adjust game difficulty based on predicted player affective states, such as challenge and flow, enhancing overall player experience [26]. These developments illustrate that effective DDA is not merely about matching difficulty to performance metrics but involves a complex understanding of player behavior, preferences, and emotional states. As AI continues to advance, DDA systems are poised to become more responsive and personalized. Consequently, they contribute to more immersive and satisfying gaming experiences.

4.2 Procedural content generation in progression

Procedural content generation involves using algorithms to generate game assets such as maps, levels, enemies, and quests. With the use of AI, PCG can also incorporate player preferences, progress history, and behavior patterns to produce content that evolves alongside the user [27]. For example, No Man’s Sky uses a mathematical model and seeded randomness to generate over 18 quintillion planets with unique flora, fauna, and atmospheric conditions [28]. Roguelikes like Hades or Dead Cells use PCG to create dungeon layouts that adjust in difficulty and complexity as the player progresses.

Recent approaches in PCG via machine learning (PCGML) have allowed for the generation of levels that adapt not only structurally but also semantically. In other words, the content serves specific learning objectives or engagement patterns [16]. Reinforcement learning, generative adversarial networks (GANs), and Markov decision processes (MDPs) are among the key techniques enabling these innovations. For instance, Khalifa et al. introduced PCGRL that is a framework that uses reinforced learning to train level-designing agents [29]. This enables the creation of game levels that adapt to player performance and preferences. Meanwhile, Liu et al. provided a survey on the use of deep learning for procedural content generation while highlighting methods like convolutional neural networks and recurrent neural networks for generating complex game elements [30]. These innovations signify a shift toward more intelligent and adaptive content generation in games. By integrating machine learning techniques, PCG systems can produce content that not only varies structurally but also aligns with the semantic and experiential aspects of gameplay, offering personalized and engaging experiences for players. As research in this area continues to evolve, we can anticipate even more sophisticated PCG systems that seamlessly blend algorithmic generation with player-centric design principles.

4.3 Personalized progression paths through AI

One of AI’s most promising contributions is in supporting individualized player journeys. AI can segment players based on behavioral telemetry such as movement patterns, combat preferences, and exploration tendencies and provide progression paths that match these patterns [31]. This is not just difficulty scaling; it allows for changes in narrative delivery, mission objectives, upgrade paths, and even the emotional tone of gameplay. The Nemesis system of Middle-earth: Shadow of Mordor is a prime example. In that system, AI-generated enemies evolve in personality and tactics based on previous encounters. In educational games, intelligent tutoring systems can adjust challenge levels to align with Bloom’s taxonomy of cognitive skills that results in higher learning retention and flow [32].

Furthermore, the role of AI in player segmentation has been important in understanding and predicting player behavior. By analyzing in-game actions and preferences, developers can create more engaging and personalized experiences. This approach not only enhances player satisfaction but also informs game design decisions to cater to diverse player profiles. Zhu and Ontañón present a player-centered framework of AI for game personalization as they identify open problems and emphasize the need for collaboration between technological advancement and player experience design [33].

Overall, the integration of AI into game design paves the way for the creation of personalized progression paths that adapt to individual player behaviors and preferences. By leveraging behavioral telemetry, procedural content generation, and adaptive learning frameworks, AI enables dynamic and engaging experiences that evolve with the player. This marks an important shift from static game design to responsive, player-centric models.

4.4 The use of AI in reward systems

AI also transforms how players are rewarded by optimizing the timing, frequency, and type of feedback. Unlike traditional fixed-reward schedules (e.g., get 10 XP for every kill), AI-driven systems use engagement prediction models to identify when a player is at risk of disengaging and deploy appropriate incentives. These might include offering bonus objectives, providing rare loot, or introducing surprise narrative content [34]. For example, Destiny 2 dynamically adjusts loot drops based on a player’s recent reward history to prevent monotony or “loot fatigue.” Machine learning models trained on user retention metrics can detect when players are likely to churn and introduce corrective mechanisms such as progression boosts, narrative reminders, or social incentives.

Importantly, these mechanisms require careful calibration to avoid creating manipulative or addictive loops. While reinforcement learning and behavioral modeling are powerful, designers must be cautious not to exploit player vulnerabilities in pursuit of engagement metrics. To address these concerns, researchers advocate for the integration of ethical frameworks within AI systems. By incorporating machine ethics into reinforcement learning, developers can ensure that AI agents make decisions aligned with human values and norms. Thus, they can promote responsible and sustainable gaming practices [35].

4.5 Ethical considerations in AI-driven progression

The use of AI in progression systems raises critical ethical issues. A well-designed AI system should empower players. However, concerns arise when AI is used to impose artificial barriers such as slowing progression or manipulating challenge levels to steer players toward monetized options like microtransactions or paid boosters. Opacity is another issue as players are often unaware that an AI system is shaping their experience. Without transparency, trust in the system may erode, particularly in competitive environments where fairness is of crucial importance. The black-box nature of some AI systems makes it difficult for players to understand how decisions are made. This can further increase concerns about fairness and accountability.

Best practices suggest giving players visibility and control over AI-driven systems, including the ability to opt out of personalization or toggle transparency modes. Game designers must also navigate data privacy concerns, especially when integrating biometric or affective feedback systems into progression logic. The principles of explainable AI (XAI) and human-in-the-loop (HITL) design are becoming increasingly relevant in games to ensure that progression systems remain comprehensible and ethically sound [36]. Furthermore, data privacy is an important concern, mainly when AI systems use biometric or affective feedback to inform progression logic. Ensuring that players have control over their data and the ability to opt out of personalization features is essential to maintain ethical standards and comply with data protection regulations [37].

5.1 The shift toward AI-driven progression

Artificial intelligence is increasingly integral to the structuring and delivery of player progression. Moving beyond static skill trees and predefined leveling systems, future games are anticipated to use AI to create adaptive and personalized experiences. AI-driven progression systems can analyze player behavior in real time, adjusting skill recommendations, challenge levels, and rewards to align with individual playstyles. This paradigm shift from fixed RPG character development to adaptive progression models ensures that each player’s experience is unique. For instance, an AI assistant in a role-playing game could learn from a player’s combat style, preferred weapons, and movement patterns. Thus, it provides real-time suggestions that evolve alongside the player’s behavior. If a player frequently utilizes ranged attacks but struggles in melee combat, the AI could recommend ranged perks, suggest strategies, or adjust enemy behavior to accommodate the player’s strengths and weaknesses.

However, ethical considerations must be addressed in the design of such systems. AI-driven progression should enhance player agency rather than diminish it. Overly deterministic AI-generated suggestions may undermine the sense of accomplishment and personal investment, leading to a diminished gaming experience. Therefore, it is imperative to balance AI adaptability with meaningful player choices to maintain engagement and satisfaction.

5.2 Meta-progression and cross-game continuity

The transition toward cloud-based ecosystems and live-service models has elevated the relevance of meta-progression. These are progression systems that persist beyond a single game session, platform, or even franchise. Instead of restarting character growth with each new game, future progression models may enable players to maintain a persistent progression profile that carries over across multiple games. This concept was exemplified in the Mass Effect series (2007–2012), where players could transfer saved games between titles, allowing previous narrative choices to influence subsequent storylines. Looking forward, the development of interconnected universes could facilitate similar continuity on a broader scale.

An emerging and contentious area of in-game progression is blockchain-based persistence, wherein player-owned progression assets such as skills, ranks, and digital items exist independently of specific game servers. In theory, blockchain technology could enable ownership of in-game progression elements, cross-game asset transfers, and decentralized progression tracking. This approach could enhance player autonomy by allowing assets to retain value across different gaming environments. However, it also raises concerns regarding monetization, accessibility, and potential exploitation. The risk of pay-to-win economies necessitates careful consideration of the ethical implications of integrating blockchain into progression systems.

5.3 Procedural and AI-generated story progression

AI-driven narrative design is expected to redefine the nature of progression in story-rich games. Procedural quest generation, previously limited to fetch quests or randomized events is becoming more sophisticated through natural language processing (NLP) and large language models (LLMs) [39, 40]. These systems can dynamically generate branching story arcs, context-sensitive dialog, and evolving world states based on player actions.

In future games, the decisions of the players could not only shape immediate outcomes but also generate new plotlines and character relationships in real-time. Progression in such systems would be less about completing pre-authored content and more about co-creating narrative paths that reflect the unique playstyle, values, and history of the individual player.

5.4 Gamification beyond entertainment

The principles of player progression are increasingly being applied outside of entertainment in areas such as education, workplace training, mental health, and civic engagement. Serious games and gamified applications already use progression systems to motivate learning and behavior change [41]. With the support of AI, these systems can become more adaptive and impactful. For example, educational platforms may use procedural skill trees to guide students through personalized curricula. Fitness apps can deploy AI to recommend workout goals and dynamically adjust difficulty based on past performance. Even civic apps can track social contributions or sustainable behavior through progression metaphors like leveling or badges.

In these contexts, progression becomes a tool for long-term behavior reinforcement, but it must also preserve intrinsic motivation and avoid excessive gamification that may trivialize real-world issues.

5.5 Ethical considerations in future progression systems

As progression systems become more complex, persistent, and data-driven, they must adhere to evolving standards of digital ethics and regulation. Regulatory frameworks such as the General Data Protection Regulation (GDPR) in Europe already impact how games handle player data, especially in AI-driven personalization [42]. Designers and developers will need to establish best practices for fairness, transparency, and explainability. Player autonomy must remain a central design goal, particularly when progression systems adapt based on biometric or behavioral data.

Moreover, there is a growing demand for ethical AI audits and third-party validation of adaptive systems in games, especially those involving vulnerable populations such as minors. The future of player progression will depend not only on technological capabilities but also on the industry’s commitment to responsible innovation.

Overall, the following can be stated:

• AI should be used to enhance progression, not throttle XP gain artificially to encourage microtransactions.

• Games must ensure that progression remains rewarding rather than a grind that feels endless with no meaningful payoffs.

• AI-driven skill recommendations must suggest and not force progression paths. This can allow players to experiment and find their own playstyle.